{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Fraud Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features V1, V2, ... V28 are obtained with Principle Component Analysis transformation due to confidentiality issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mibiy\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n",
      "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
      "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
      "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
      "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
      "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
      "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
      "\n",
      "         V8        V9  ...         V21       V22       V23       V24  \\\n",
      "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
      "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
      "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
      "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
      "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
      "\n",
      "        V25       V26       V27       V28  Amount  Class  \n",
      "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
      "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
      "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
      "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
      "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
      "\n",
      "[5 rows x 31 columns] None\n"
     ]
    }
   ],
   "source": [
    "# import dataset\n",
    "df = pd.read_csv(\"creditcard.csv\")\n",
    "print(df.head(5), df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the percentage of fraud in all transactions is  0.173 %\n"
     ]
    }
   ],
   "source": [
    "# see percentage of fraud in all transactions # class=1 - fraud; class=0 - non-fraud\n",
    "fraud = df[df.Class==1].index\n",
    "nonfraud = df[df.Class==0].index\n",
    "fraud_num = len(fraud)\n",
    "nofraud_num = len(nonfraud)\n",
    "fraud_perc=round(fraud_num/(fraud_num+nofraud_num),5)*100\n",
    "print(\"the percentage of fraud in all transactions is \", fraud_perc, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Correlation Heatmap')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ETBMSpjxMjTq1yc05Ttaeg8zq9pjXurZWL3k+SBp7B808Y+2Toa+R7Mll0Jyn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\np6aLiIiIiIiIOEgLcREREREREREHefLUdBEREREREblI+h7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/TYu4KOU4kAMQ7Yuj4Mjx\nC1nZeUQnVcyKTorDn13z/tTEyXpqkpN7HF9i669+T0psTU5u6KfTeanPNNfcXY9TOV6bb5oHoedo\nbKrfn5qoHnfX47Wc8NatKck9+tXvJbm5hLVuXaFNk46dCGuTSPGG9SHtf2VxvniOH7nwHul49nHi\nk+Krbd8yJpK+g69j94c7LypXLk+uWIhba48DG4FbAjeNAhZZa8v/CWMUpUfO680YEyT8YrZYTQ5V\nc2ylnGD7UrmNchonJ7ChINupElZ7m5oinKynBsH2OehzoTYe6jPNteBt6hbW+PU4leO1+aZ5EHqO\nxqaGNjVFqJ6gbeoW5o3njrM5tbx/N4aoH/yIwrkvhLDNapJCGOew8DAe/u2jLP/fv3D0UM5FZ8vl\nx00f1lZ2evrbgf/eX3aHMSYZ6A68V92DjTFjgbEA97bqT3pkpyptTmXlEdn2wl+1WibHczrnRAPt\n/gX+7Dyiky/kRCfHU1gpx5+VR0zbeAqz8zDhYTSPbsmZ/ELlNGJO73sG02NUOgDZO/cR0zaBL8qy\nfPEUHs2vuj++yvtTsc3XWU9d+RJbk330wl93c44eI7F13U6h8mqfaa65sx6v9ZvXcrw4DzQ25ffH\nPc8d1ePu547T4/PlsVzC2iR+9XtYmzaUHL/wvsa0bEmTlBTiZj9fen98PK1mPsXJn06u0we2Db3n\n2wwaNRSAvTs/J6HthaPtCb4EThwNfjnf2Kd/SPb+LJb9zztB7xepjSuOiAcsAQYZY/oALay1W8vd\n9x/AW9bac9U92Fo7z1rbz1rbL9giHOD49n1Ep/iIurINYU3DaXf7AA6t2Bq07cXI2rGPuBQfrQI5\nXW4bQObKijmZq7bSbcQNAFyT0Z+DH32snEbO2fbyKhZmTGFhxhQ+X7GFriNKry9K7t2Bs/7TnKr0\nD8epo/kUnzpDcu8OAHQdMZDMlVtcU09dpQ0cwNLl72OtZcfuT4iKiqRN6+pPsyrPq32muebOerzW\nb17L8eI80Ni487mjetz93HF6fM5/+ilNvnEFYT4fNGlC8/SbOPvRh1/db0+d4thdt3P8e6M4/r1R\nnPv44zovwgFWvPwuj2WM47GMcWxa8XduHJEGQKfenTntP0X+0aoH7UZO+C4toyNZ+LMFda7jkmVL\nLv0flzIhn4LSiIwxrwOdgSXW2unlbt8APGGtXV2X7bz8jburLeobN/Xkup/dXfr1ZYv+xq7fLKXn\nhBEc37Gfwyu3ktCzPWkLHqFZq5aUnD1H0dGTLL3p8aDbyq7hfIL26T25aerdmPAwdr3+NzbMWcrA\nR0eQvXM/mau2Et68KcOee5Ckru04k1/I0ofmcPJQbl3KU04D5AAMnjmGlNQenC8q5t0J88jetR+A\nMctmsTBjCgC+7il8+9mxNIloxv41O1g19eWvpZ5xW2ZUmzFx2tNs2raT/PwCEuJj+eH3R3P+/HkA\nRt45DGsts2a/wLoNm2kREcHMyePo1qVz0G0913dqjfVcSn3mlhzwVr85VY9TOV6bb5oHoedobNz9\n3FE97n3uNHTOvR0PVZvRrP+/EPWj/8aEhVH07jJO/+kPRN57P+c++5Ti9RW/6Sj22ecpfOnFoAvx\n/86s/dr0+2eOpWdqH4oDX1+2b9deAH6x7DkeyxhHvC+BF/++gC8yD3HubOn7rfde/gt/fW1Vhe0s\n+ueSelwH6D6nf3m/exaL9dRy4v+4cizcthC/E3gT6GKt/TRwWzvgQ+BKa+v2J42aFuINqaaFuEhD\nqmkh3pBqW4iLiIiINIaaFuINpS4L8Yaihbh7uHUh7qqlpLX2LSp9IoO19gDwja9lh0RERERERC5X\nLv76r0udm64RFxEREREREfE8LcRFREREREREHKSFuIiIiIiIiIiDXHWNuIiIiIiIiLiDLXHv139d\n6nREXERERERERMRBWoiLiIiIiIiIOEgLcREREREREREH6RpxERERERERqUrfI95odERcRERERERE\nxEFaiIuIiIiIiIg4yJOnpu9r4swpFC0xjuSIPNd3qiM547bMcCTHqXpERETk0vDkXl+jZ/yrbdro\nGXJpMsbcAvwaCAd+b619Okib/wCmAxbYYa397sVkenIhLiIiIiIiIhfJev97xI0x4cDvgCHAYWCT\nMWaptfbjcm06AU8A11trTxhjEi82V6emi4iIiIiIyOWqP5Bprd1nrS0GXgNur9Tmv4DfWWtPAFhr\nj15sqBbiIiIiIiIicrn6BnCo3O+HA7eV1xnobIz50BizIXAq+0XRqekiIiIiIiLiScaYscDYcjfN\ns9bOK98kyMMqf+hYE6ATkAZcAaw1xnSz1ubXd7+0EBcREREREZGqPPA94oFF97wamhwGriz3+xXA\nkSBtNlhrzwH7jTGfUbow31Tf/dKp6SIiIiIiInK52gR0MsakGGOaAaOApZXaLAHSAYwxrSk9VX3f\nxYRqIS4iIiIiIiKXJWvteeAh4D3gE+B1a+0eY8wMY8zwQLP3gOPGmI+B1cBEa+3xi8nVqekiIiIi\nIiJy2bLWLgOWVbptarn/t8CjgZ8GoYW4iIiIiIiIVFXi/e8R/7ro1HQRERERERERB12WR8S/Pf0e\nOqX35FxRMUsmvETW7gNV2iR3a8cdzz5I04imfL56B+9OfznknEHTR9M+vRfnis7y7oR55ATJSerW\njoxnH6BJRDP2rd7O+9NfcWVOSmoPBk0bjQkPY+dra/j7i+9UuD+8WROGzX6QpO4pFJ3ws/ShORQc\nPhZyLV7LAe/Mg588NZsPPtxIfFwsS/4wt8r91lp+/vxc1q7fREREc2ZNGc+113QMuQ6vzQHNNXfn\naB64e3ycyvFSLZrT7h4fp3K8OD4jpt3Ltem9KS46yx8nvMjhPfurtBk2YST977qRlq2imNh1TMgZ\nV6f2IHV6ab/teW0Nm1+o2m9Dn3uQxO4pnDnhZ9mP5uCvZ7+JuOKIuDFmjTHm5kq3PWKMecEY84wx\nZo8x5hNjzG+MMcG+563OOqX3JD7Fx29Sx/POEwsY9uR9QdvdOut+3nni9/wmdTzxKT46pvUMKad9\nek/iUnzMTx3Pe08sYMiT9wZtN3TWfbz3xALmp44nLsVHSloP1+WYMMPgmWP485hnWDB4El2GDyCh\nU9sKbbqPTOPMyVPMTx3P5gXLSXt8VEh1eDEHvDUP7sgYwtzZT1Z7/9r1mzh4+AjLFi1g+qSHmfmr\nOSHVAN6bA5pr7s7RPHD3+DiV46VaNKfdPT5O5XhxfK5N60WbFB8z037Mosnz+Y9Z3w/abs/7W3n2\n9imhlgGU9lvak2NYMuYZXhk0ic7DBxBfqd+6jkzj7MlTLLxxPNt+v5yBT9Sv3y4pJfbS/3EpVyzE\ngVcp/Zj48kYBi4DrgR5AN+A6IPVigq4Z0pcdi9cCcHhbJhExLYlKjK3QJioxluZRLTi8NROAHYvX\n8s2hfUPK6TikL3sWrwMga9teImIiiayUE5kYS7OoFhwJ5OxZvI5OQ/u5Lie5VwfyD+Rw8lAuJee+\n5JN3NtBxSMX+6DSkD7sD/frZso1cdX3XkOrwYg54ax7069WdVjHR1d6/et0Ght8yCGMMPbt1we8v\nJPdYXkh1eG0OaK65O0fzwN3j41SOl2rRnHb3+DiV48Xx6T70Oja++QEAB7Z9TovoSGLaxFZpd2Db\n5xTk5tenFJJ6deDkgRwKDpb22z/e2UD7Su//2w/tw8dvlPbb58s2cmU9+00E3LMQfwO41RjTHMAY\n0w5oCxQDEUAzoDnQFMi5mKAYXzwFRy580nxBdh4xSXEV2yTFUZB9YQFRkJVHjC8+pJxoX1yFHH92\nHtGVcqKT4vCXy/Fn5RHtq9jGDTlRvjj8WTU/PsoXR8GR0jb2yxLO+k/TIi4qpFq8lgPemge1yck9\nji+x9Ve/JyW2Jic3tNO1vDYHNNfcnaN5UP3+XE45XqpFc7r6/bmccrw4Pq2S4sgvl5OffZxWIb43\nr02ULw7/kQv7WZiVR1SlWiJ9cRRW6reIevSbCLhkIR74DraNwC2Bm0YBi6y16yn9nraswM971tpP\nLiosyIntpZ9GX75N1UZV2tSaU4dtXCI5JkinVY2ovc3llhPYUJDtXJrzoDbBthXqlSRemwOaa+7O\n0Tyooc3llOOhWjSna2hzGeV4cXyCvp9owPcwgZBaI4LvR8Puhlw+3PRhbWWnp78d+O/9xpiOQBfg\nikCblcaYG621H1R+sDFmLDAW4Nb4/vSNuvAhUdfdM4S+o9IB+GLnPmLaJnx1X4wvHv/RiqewFGRX\nPAIekxyPP+dErQX0vmcwPQI52YGcLwL3RfviKayU48/OI7pcTnRyPIU5tZ9O41ROhccnV358xf7w\nZ+UR0zaewuw8THgYzaNbcia/sM4ZXsrx6jyojS+xNdlHLxwBzzl6jMTWCTU8oiqvzAGncrw21/Ta\npnngVI6XaqnyeM1p142P1+aBU/XcMHoo3/rOIAAO7thLbLn377G+BE7W4b15KAqz8ohue2E/o5Lj\nOXX0RJU2URf5/LnkWH19WWNxxRHxgCXAIGNMH6CFtXYrcCewwVpbaK0tBN4FBgR7sLV2nrW2n7W2\nX/lFOMCml1cyN2MyczMm8+mKzfQccQMAV/TuyFl/UZUXjMKj+Zw9VcQVvUu303PEDXy2ckutBWx7\neRULM6awMGMKn6/YQtcRAwFI7t2Bs/7TnKqUc+poPsWnzpDcuwMAXUcMJNNFOWWyduwjLsVHqyvb\nENY0nC63DSBz5dYKbTJXbaVboF+vyejPwY8+rvP2vZbj1XlQm7SBA1i6/H2stezY/QlRUZG0aR3a\naWNemQNO5Xhtrum1TfPAqRwv1VKe5rQ7x8dr88Cpeta+soJnMh7jmYzH2LliE/3vuhGAdr07ccZ/\nut7XglcnZ8c+YlN8xAT6rfNtA9hXqd/2rdzKtf9W2m+dMvpzqB7PH5EypiFPTb1YxpjXgc7AEmvt\ndGPMSOC/KD1l3QDLgeette/UsBmmX/29GovKmHkvHVN7cK6omLcnvMSRXaVff/DgsqeYmzEZgLbd\nU7gj8DULmWt2sGzqwirbaRnsPPdyBs8cQ0pqD84XFfPuhHlkB3LGLJvFwozST3T0dU/h28+OpUlE\nM/av2cGqqaF/TZoTOe3Te3LT1Lsx4WHsev1vbJizlIGPjiB7534yV20lvHlThj33IEld23Emv5Cl\nD83h5KHckGvxWg5cWvNg3JYZ1W5/4rSn2bRtJ/n5BSTEx/LD74/m/PnzAIy8cxjWWmbNfoF1GzbT\nIiKCmZPH0a1L56Dbeq7v1GpzvDYHNNfcnaN54O7xcSrHS7VoTrt7fJzKuRTH57A5V2POv8+4ny6p\nPSkuKuaPE1/k0K59AExa9gueyXgMgOGPf49+t19f+nlPOSdYv+ivvPv8G19to0NJ0xoz2qX35MZp\npf328aK/sWnOUgY8OoKcXfvZv7K0325+/kHaBPrt3YfmUHAweL/9+OAfLuqbntzi1E//wz2LxXqK\nnPm6K8fCbQvxO4E3gS7W2k+NMeHAC8CNlF6Bsdxa+2ht26ltId5QaluIi1xqalqIN6SaFuIiIiJy\n+altId4QaluINyQtxN3DrQtxN10jjrX2Lcp9nJq19kvgga9vj0RERERERC5TLv4e7kudm64RFxER\nEREREfE8LcRFREREREREHKSFuIiIiIiIiIiDXHWNuIiIiIiIiLiDLdH3iDcWHREXERERERERcZAW\n4iIiIiIiIiIO0kJcRERERERExEG6RlxERERERESq0veINxodERcRERERERFxkBbiIiIiIiIiIg7S\nQlxERERERETEQZ68RjwC83Xvgsgl6bm+Ux3JGbdlhiM5TtUjIiIiFyfWgWXJvrDzjZ7hObpGvNHo\niLiIiIiIiIiIg7QQFxEREREREXGQJ09NFxERERERkYtkS77uPfAsHREXERERERERcZAW4iIiIiIi\nIiIO0kJcRERERERExEG6RlxERERERESq0teXNRodERcRERERERFxkBbiIiIiIiIiIg7SQlxERERE\nRETEQZflNeKDp4+mQ3ovzhWd5S8T5pGz+0CVNknd2jHs2QdoGtGMvau3s2r6KyFlpKT2YNC00Zjw\nMHa+toa/v/hOhfvDmzVh2OwHSeqeQtEJP0sfmkPB4WP1qmfQ9NG0D9Tzbg31ZDz7AE0imrFv9Xbe\nD7Eep3Kc6jcv5XipFoCfPDWbDz7cSHxcLEv+MLfK/dZafv78XNau30RERHNmTRnPtdd0DDkHvDWn\nnarHqRz1m7v7zWs5Ghv35oC3xsepepzKcbLfMqbdQ6f0npwrKuatCS+RtadqPcnd2nHXrx6kSURT\nPl+9g2U/eznknLumjeHa9N6cKzrLHye8yOEgOcMmjOS6u26kZatIJnW9N/RiLiFW14g3GlccETfG\nrDHG3FzptkeMMS8YY35hjNkd+Bl5sVnt03sSl+LjpdTxLH9iATc/eW/QdjfPuo/lTyzgpdTxxKX4\naJ/Wo84ZJswweOYY/jzmGRYMnkSX4QNI6NS2QpvuI9M4c/IU81PHs3nBctIeH3VR9cxPHc97Tyxg\nSDX1DJ11H+89sYD5gXpSQqjHqRyn+s1LOV6qpcwdGUOYO/vJau9fu34TBw8fYdmiBUyf9DAzfzWn\nXjlemtNO1eNUjvrN3f3mtRyNjXtzwFvj41Q9TuU42W+d0nqSkOLj12njWTp5AbfNui9ou9uevJ+l\nk3/Pr9PGk5Dio1Naz5Byrk3rRZuUZJ5Me4TXJs/n32f9Z9B2u9/fwuzbp4Rch0h5rliIA68ClZ+Z\no4AcoA/QC/gXYKIxJuZigjoN6cvuxesAOLJtL81jIolMjK3QJjIxluZRLTiyNROA3YvX0Wlovzpn\nJPfqQP6BHE4eyqXk3Jd88s4GOg7pW2k/+rB78VoAPlu2kauu71qvejoO6cueQD1Z2/YSUU09zcrV\nsyfEepzKcarfvJTjpVrK9OvVnVYx0dXev3rdBobfMghjDD27dcHvLyT3WF7IOV6a007V41SO+s3d\n/ea1HI2Ne3PAW+PjVD1O5TjZb98c2pftb5Zu5/C2TCKiWxLVpmI9UW1iaR7dgkOBera/uZZvDu1b\nZVs16Ta0H5ve/ACAf27LpEV0S2Iq5ZTdV5CbX59SRL7iloX4G8CtxpjmAMaYdkBb4DTwN2vteWvt\nKWAHcMvFBEX74vAfOf7V7/7sPKKT4iq2SYrDn33hjb0/K49oX8U2NYnyxeHPqvnxUb44Co6UtrFf\nlnDWf5oWcVEh1QKl9RQ0cj1O5TjVb17K8VItdZWTexxfYuuvfk9KbE1ObuinuXlpToNeC9Rv3ns9\ncCpHY+PeHPDW+IBec+rbbzFJ8ZwsV09Bdh4xlbJifHEUlNufgqw8YpLiQ8qJTYonv1zOyew8WvlC\n24ZIXbliIW6tPQ5s5MIiexSwiNKF97eNMS2NMa2BdODKiwozJlh+rW2o3KamCIJlVI6ovU3dwupX\nT5U2Lshxqt+8lOOlWuoq2JwKll0rD83pwIaCbEevBbWHqd/c+nrg2DzQ2Lg2J7ChINu5NMcnsKEg\n29FrTq1ZQd+aN+z799Jt1CHnclNiL/0fl3LTh7WVnZ7+duC/91trtxpjrgM+AnKB9cD5YA82xowF\nxgLcGd+f/lGdvrqvzz2D6TkqHYCsnfuIbpvw1X3RvngKj1Y8tcSfnUd0ub9+RSfH48+p++kn/uw8\nopMrPr4w50TFNll5xLSNpzA7DxMeRvPolpzJL6zT9nvfM5gegXqyd+4jpm0CX4RYT2Ed6nEqp8Lj\nG7HfvJjjpVrqypfYmuyjF46A5xw9RmLrhBoecYHX5rReC9RvVR7vodeDxszR2Lg7x2vjo9ec+vVb\n/9FD6Pud0nq+2LGPVuXev8f4qr43L8jKI6bc/sQkx1NwtOL+BDNw9FC+9Z2bADi4Yy+x5XJa+eIp\nyKl9GyL14Yoj4gFLgEHGmD5AC2vtVgBr7SxrbS9r7RBK/071ebAHW2vnWWv7WWv7lV+EA2x9eRX/\nmzGF/82YwucrttBtxEAA2vbuwFn/aU5VemE6dTSf4lNnaNu7AwDdRgzk85Vb6lxI1o59xKX4aHVl\nG8KahtPltgFkrtxaoU3mqq10G3EDANdk9OfgRx/XefvbXl7FwowpLAzU0zVQT3It9SQH6uk6YiCZ\ndajHqZwyjd1vXszxUi11lTZwAEuXv4+1lh27PyEqKpI2ret22pjX5rReC9Rv5Xnt9aAxczQ27s7x\n2vjoNad+/bbxlZW8mDGZFzMm8+mKzfS6q3Q7V/TuyBl/EYWVrtEuzM2nuLCIK3qXfpNKr7tu4NMV\ntdez7pUV/DLjcX6Z8Ti7VmzmurtuBODq3h054z+ta8Gl0Rg3nW5hjHkd6AwssdZON8aEA7HW2uPG\nmB7An4Be1tqgR8XLPH313TUWNWTmGNqn9uBcUTHLJswje9d+AO5bNov/zSj9BERf9xSGPTu29Osc\n1uxg5dSqX39Q018x2qf35Kapd2PCw9j1+t/YMGcpAx8dQfbO/WSu2kp486YMe+5Bkrq240x+IUsf\nmsPJQ7k17Xa1Bs8cQ0pqD84XFfNuuXrGLJvFwnL1fDtQz/41O1gVpB435DjVb17KuRRrGbdlRrU5\nE6c9zaZtO8nPLyAhPpYffn8058+XPuVH3jkMay2zZr/Aug2baRERwczJ4+jWpXPQbT3Xd2qNNXlp\nTjtVj1M56jd395vXcjQ27s0Bb42PU/U4ldOQ/VZoal6TDJtxL50C79/fmvgSRwL1/GDZU7yYMRmA\ntt1TuPNXpV8//PmaHfxl2sIK2zjJl7XW9G8z7qNLai+Ki87yp4lzObRrHwATlz3NLzMeB2D449+l\n7+3XE5MUR0HOCdYvWs3y59+osJ1fH3itHtfNuY//4Vvds1isp+jf/J8rx8JtC/E7gTeBLtbaT40x\nEUDZn9YKgAettdtr205tC/GG4qbTCUQuJTUtxBtSbQtxERERcYfaFuINoS4L8YbimYX4QxnuWSzW\nU/ScZa4cCzddI4619i3KfUyCtfYMcO3Xt0ciIiIiIiIiDUsHdUVEREREREQc5Koj4iIiIiIiIuIS\nLv76r0udjoiLiIiIiIiIOEgLcREREREREREHaSEuIiIiIiIi4iBdIy4iIiIiIiJV6RrxRqMj4iIi\nIiIiIiIO0kJcRERERERExEFaiIuIiIiIiIg4SNeIi4iIiIiISBXW6hrxxuLJhXhkiTM5RTqfQKRe\nnus71ZGccVtmOJLjVD0iIiJelc/5Rs/4Z8mpRs8QqSstJUVEREREREQcpIW4iIiIiIiIiIM8eWq6\niIiIiIiIXCR9j3ij0RFxEREREREREQdpIS4iIiIiIiLiIC3ERURERERERByka8RFRERERESkKl0j\n3mh0RFxERERERETEQVqIi4iIiIiIiDhIC3ERERERERERB+kacREREREREanC6hrxRnPZLcSvSuvB\njdNHY8LD+PjVNWx54Z0K94c1a8LQ5x+kTfcUzpzws/yHc/AfPhZyTkpqDwZNK83Z+doa/v5ixZzw\nZk0YNvtBkrqnUHTCz9KH5lBQjxyAQdNH0z69F+eKzvLuhHnk7D5QpU1St3ZkPPsATSKasW/1dt6f\n/oor61G/hV6PxqZ+Y/OTp2bzwYcbiY+LZckf5la531rLz5+fy9r1m4iIaM6sKeO59pqOIdei8XHv\nc8fJHFC/uTnHS2OjeupXj3LcnzNi2r1cm96b4qKz/HHCixzes79Km2ETRtL/rhtp2SqKiV3HhJwB\n8F8/G0vf9H6cLTrLr8c/z77deyvc3yyiOY+9+Di+q32UlJSwadVGXn56Yb2y5PLm6Knpxpg1xpib\nK932iDGvAjHnAAAgAElEQVTmBWPMcmNMvjHm/yrdn2KM+bsx5nNjzCJjTLN654cZ0p4cw9J7nuGP\nN02i8+0DiOvUtkKbrqPSOJN/ilduGM/23y/n+smj6pUzeOYY/jzmGRYMnkSX4QNIqJTTfWQaZ06e\nYn7qeDYvWE7a46HnALRP70lcio/5qeN574kFDHny3qDths66j/eeWMD81PHEpfhISevhunrUb6HX\no7Gp39gA3JExhLmzn6z2/rXrN3Hw8BGWLVrA9EkPM/NXc0LaPmh83PzccTIH1G9uzvHS2Kie+v+b\noBx351yb1os2KT5mpv2YRZPn8x+zvh+03Z73t/Ls7VNC2nZ5fdP7kdyuLQ/eOJbfPT6HH8z6YdB2\nS+a9yY9u+gHjvv1jvtnvWvqk9a13ply+nL5G/FWg8ivxqMDtvwRGB3nML4DnrLWdgBNA8GdeHST1\n6kD+gRwKDuZScu5L/rF0A+2HVnzipAztw6dvrAUg8y8bueL6riHnJAdyTh4qzfnknQ10HFIxp9OQ\nPuxeXJrz2bKNXFWPHICOQ/qyZ/E6ALK27SUiJpLIxNgKbSITY2kW1YIjWzMB2LN4HZ2G9nNdPeq3\n0OvR2NRvbAD69epOq5joau9fvW4Dw28ZhDGGnt264PcXknssL6QMjY97nztO5oD6zc05Xhob1VP/\nfxOU4+6c7kOvY+ObHwBwYNvntIiOJKZNbJV2B7Z9TkFufkjbLq//0H9h9eK/AvCPbZ8RGRNJXGJc\nhTbFZ86ya/0uAM6fO8++3XtJSG5d70zXK7GX/o9LOb0QfwO41RjTHMAY0w5oC6yz1r4P+Ms3NsYY\n4KbA4wAWAnfUNzzSF0fhkQtvpAuz8ojyVXxyRfni8Afa2C9LKPafJiIuKqScKF8c/qwLOf6sPKKD\n5BSUyznrP02LEHMAon1xFBw5fiErO4/opIpZ0Ulx+LNr3p+aOFWP+i30ejQ21e/PxcrJPY4v8cI/\nrEmJrcnJDe20So1P9ftTE/Vb9ftTE6/1m1M5XhobUD3V7Y9yLu2cVklx5JfLyc8+TitffEjbqIsE\nXwLHsi78W38s+zgJvoRq20fGRHLd4P7s/HB7g++LeJ+jC3Fr7XFgI3BL4KZRwCJrbXV/qkgA8q21\n5wO/Hwa+Ud/80nV95X2q0qrqA0P8Q4oJso3KOXXbl7qEBdtOlbDa29QU4VA96rfgbb7uDCdzAhsK\nsp2GHZu6CLa9YDXWRONTQ5uaItRv1bepKcJj/ebY+HhobAIbCrId1aOcSzsn6L+/DfzvPlQ3t4Pn\nhIWHMf63E/m//11KzsGcBt8X8b6v48Payk5Pfzvw3/traBvsXW/QZ4MxZiwwFmBkbH+uj+pUpU1h\nVh5RbS/89SwqOZ5TOScqtsnOI7ptPKey8zDhYTSLbsmZ/MKaK6rEn51HdPKFnOjkeAor5fiz8ohp\nG09hIKd5CDm97xlMj1HpAGTv3EdM2wS+KMvyxVN4tOIpOf7sPKJ9lfen7qftNHY9TuV4sd80NuX3\np/6nogXjS2xN9tELfxXPOXqMxNbV/1U8GI1P+f1x13PHiRz1m3tzvDY2qqd+9SjH3Tk3jB7Kt74z\nCICDO/YS2/bCv8GxvgROVppz9ZVxzzCGfKf0I6wyd35O63Knmbf2JZCXE/yytB89/d9kHTjCOwuW\nNsh+yOXn6/ge8SXAIGNMH6CFtXZrDW2PAbHGmLI/GFwBHAnW0Fo7z1rbz1rbL9giHCBnxz5i2/mI\nubINYU3D6Tx8APtXVozfv3Ir3/y3GwDoOKw/hz/8OKTiALJ27CMuxUerQE6X2waQWSknc9VWuo0o\nzbkmoz8HP6p7zraXV7EwYwoLM6bw+YotdB0xEIDk3h046z/NqUovgKeO5lN86gzJvTsA0HXEQDJX\nbnFNPU7leLHfNDb1G5u6SBs4gKXL38day47dnxAVFUmb1qGdBqfxce9zx4kc9Zt7c7w2NqqnfvUo\nx905a19ZwTMZj/FMxmPsXLGJ/nfdCEC73p044z99UdeCl7fs5b8w7tsPM+7bD7PhvfWkj7gJgM69\nr+GU/zQnjlZd8H9vwt20jG7J76fPb5B9cLUSD/y4lGno0znrFGrM60BnYIm1dnq529OACdbaW8vd\n9mdgsbX2NWPMXGCntfaFmrb/2yvvrraoq9N7csP0uwkLD+PjRX9j82+X8i/jR3B05372r9xKePOm\nDHn+Qdp0a8fZ/EKW/2gOBQdzg26rqIY/Y7RP78lNU+/GhIex6/W/sWHOUgY+OoLsnfvJ/P/s3Xl8\nFdX9//HXSVgCJEAWkpu0+iUsKrIvWlrRJEAQg1Ip7Vfaiqm1RWppK4JUpD+kAtVqBdtSpFDqF5dv\n1YpSsAgCX1BQkFUW97AUkCQsIXADSVhyfn8kLDe5WW5IJjeT9/Px4KG5c+6853POzJDDzNy7sjhn\nyMzRxHVuS0FuHovHzOLEAf85lRk4NZ3EpG6cyz/D2+PnkrWz+Osc0pdOZ0Fa8SdHeromctszo2gU\n1oS9a7azcvILAWU4VY/6LfB6NDblj83YLY+Xm/HwY0+yadsOcnNPEh3VmgfuG8m5c8VPwdw1bAjW\nWqbPmM26DZtpFhbG1EfH0qXTNX7XNbP35HJzND7Be+w4mQPqt2DOcdPYqJ7q1aOcus85aM5WmPO9\nx39Mp6TunMk/w8sPP8eBnXsAmLD09zyV9msAhj7yQ/p8+yZaxkVyMvs461/9P95+9vWL6/hP0alK\n67l/6mh6JvemML+QP49/lowdxR8yN/PtPzH2tl8S7Ynm7xsXcODLA5w9U7zNSxe8xYpX3vFZz7/2\nvxXY82xB6sTIAcH7aWdV1OrFVUE5FnU1ER8GvAF0stZ+VvLaWuA6IBw4BtxnrV1ujGkHvAJEAduA\nu621hRWtv6KJeE2qaCIuInWvool4TapoIi4iIiKVq2wiXhOqMhGvKZqIB49gnYjXxTPiWGvfpNTz\n39bam8tpuwe40YntEhEREREREaltdTIRFxERERERkeBmg/h7uOs73VwtIiIiIiIi4iBNxEVERERE\nREQcpIm4iIiIiIiIiIP0jLiIiIiIiIiUpWfEa42uiIuIiIiIiIg4SBNxEREREREREQdpIi4iIiIi\nIiLiID0jLiIiIiIiImUV1fUGuJeuiIuIiIiIiIg4yJVXxN/imCM5A4h2JEdEqmdm78mO5Izd8rgj\nOU7VIyIi4rSv28a1njG4MLzWM0SqSlfERURERERERBzkyiviIiIiIiIicmWsvke81uiKuIiIiIiI\niIiDNBEXERERERERcZBuTRcREREREZGy9PVltUZXxEVEREREREQcpIm4iIiIiIiIiIM0ERcRERER\nERFxkJ4RFxERERERkTL09WW1R1fERURERERERBzUIK+I/+y3o7mx/w0U5BfyzEPPkLFrt8/ypmFN\nmTTnURL+K56i80VsWPkhf3/y+YAyEpO6MeCxkZjQEHa8soYPn1viszy0SSOGzBhNXNdE8o97WTxm\nFicPHg24FqdyAAZMGUm7lB6czS/k7fFzyd61r0ybuC5tSXvmfhqFNWHP6o9YNeXFoMzR+ATeb27r\nM6dyfvO7Gbz3/kaiIluz6KU5ZZZba3ni2TmsXb+JsLCmTJ80juuv7RC09SgneI9RJ+txW46b/n5z\nKsdt+wC4a3zcluPUfhCT0p3rp6VjQkM48PL/sefPi3234/40vv7D/tjz5zlzzMuOB+dQUM39TcTR\nK+LGmDXGmFtLvfagMWa2MWaZMSbXGPNWqeVjjDEZxhhrjIm50m24IeUGvpaYwL0338cff/0nfvG7\nMX7bLfzrQn6SMooHbhtD5xuup09ynypnmBDDwKnp/DP9KeYPnECnoX2J7pjg06brXckUnDjFvKRx\nbJ6/jORHRgRci1M5AO1SuhOZ6GFe0jiWT5xP6rQf+W03aPq9LJ84n3lJ44hM9JCY3C3ocjQ+gfeb\n2/rMybG5My2VOTOmlbt87fpN7D94iKWvzmfKhF8y9Q+zAs5wW7+5LQd0bgvmHDf9/eZUjtv2AXDX\n+Lgtx7H9IMTQ+ckfs+kHT/LezeNIGHYT4dd8zafJiV37eP/WR1mX8muylnzIdZN/GHiOSAmnb03/\nB1D6yBhR8vrTwEg/73kfGAj8pyY24JuD+rJy4SoAPtv2GS1ahhMVG+nTprCgkO3rdwBw7uw5vtyZ\nQZv4qv8bQHyP9uTuy+bEgSMUnT3Pp0s20CG1t0+bjqm92LVwLQCfL93I1Td1DrgWp3IAOqT25uOF\n6wDI3LabsJYtaBHb2qdNi9jWNAlvxqGtGQB8vHAdHQdV/R8wnMrR+ATeb27rMyfHpk+PrrRqGVHu\n8tXrNjB08ACMMXTv0gmvN48jR3MCynBbv7ktB3RuC+YcN/395lSO2/YBcNf4uC3Hqf2gda8OnN6b\nRf5/DmPPnidz0QfEDfbdzpz3P6Eo/wwAuVu+JCw+KuCceqfIBX+ClNMT8deB240xTQGMMW2BBGCd\ntXYV4C39BmvtNmvtvpragBhPNEcOXbqF5GjmUaI95U+yW7RsQd+B32Db+x9VOSPcE4k389Iv0t7M\nHCI8kWXanDxU3MaeL6LQe5pmkeFVznAyByDCE8nJQ8cuZWXlEBHnmxURF4k3q+LtCYYcjU/521PX\ntbgtpyqyjxzDE3vpHBQXG0P2kcBuc3Nbv7ktB3RuC+YcN/395lSO2/YBcNf4uC3Hqf0gzBNFwWW1\n5B/Koamn/In213+QwpH/q/r8QKQ0Ryfi1tpjwEZgcMlLI4BXrbXOfRyfMf62y2/TkNAQJs76Nf96\nfjFZ+7OqHoG/jNKbUXmbYMkpWZGf9ZQJq7xNEORofCpoU97qXdZnjo5NJfyNgb/sirit39yWU7Ii\nP+vRuS0Yctz095tTOa7bB4pX5Gc99XN83Jbj3LnA34v+V5IwvB+terRj71+W+F0uUhV18WFtF25P\n/1fJf39cEys1xowCRgFc37ozXw+/6uKyO9Jv57bvF8/9v9j+BW0SLl19iomPISf7GP48+Ptf8dXe\nQ7w5f1FA2+LNyiHisltVIuKjyMs+7tsmM4eWCVHkZeVgQkNoGtGcgty8oMrpec9Auo1IASBrxx5a\nJkTz1YUsTxR5h3PLbo+n9Pb4tqnLHJ/3a3wC6je39JnTOVXhiY0h6/ClK+DZh48SGxMd0Drc1m9u\nydG5LXhz3Pb3m/a14D5GlVM/9reCzBzCEi79/dssIYrCrONl2kXf0oUODw5jw7DfUnTmXEAZIper\ni68vWwQMMMb0AppZa7fWxEqttXOttX2stX0un4QDLFnwFg8MHsMDg8fwwfL1DBw+AIDrel7Hae8p\ncg6XPcjSH76HFhHNmTPlrwFvS+b2PUQmemh1VRtCGofS6Y6+ZKzwLTNj5Va6DL8ZgGvTbmT/B58E\nXc62F1ayIG0SC9Im8eU7W+g8vB8A8T3bU+g9zalSJ9pTh3M5c6qA+J7tAeg8vB8ZK7YETc4FGp/A\n+80tfeZ0TlUk9+vL4mWrsNayfdenhIe3oE1MYM+cua3f3JKjc1vw5rjt7zfta8F9jCqnfuxvJ7bt\npkU7D82uboNpHEr8nd8ie7nvdrbs0pYuT/+Uzfc8zZmjJwPOqI9sUf3/E6yMk3eFXww15jXgGmCR\ntXbKZa8nA+Ottbf7ec8+oI+1ttKHJ2+96rYKi/r5tAfok9yHwvwCnhk3ky93fAnA7GWzeGDwGGI8\nMby86UX2f7mfs2fOArD4f5aw7JXlPusZEFL+Vat2Kd3pP/luTGgIO197lw2zFtPvoeFk7dhLxsqt\nhDZtzJCZo4nr3JaC3DwWj5nFiQNHKiutznIABk5NJzGpG+fyz/D2+Llk7dwLQPrS6SxImwSAp2si\ntz0zikZhTdi7ZjsrJ78QlDkan8D7zW19VpM5Y7c8Xm7Ow489yaZtO8jNPUl0VGseuG8k584V/wv6\nXcOGYK1l+ozZrNuwmWZhYUx9dCxdOl3jd10ze092pJ6KKCd4j1En63Fbjpv+fnMqx237ALhrfNyW\nU1P7QZeCiq9gtxnQg+unpkNoCAf/sZrdzy6i44TvcWL7Hg4v38KN/5xERKerKCy5op//1VG23PMH\nv+tKy34lsOfMgtSxO5KcnyzWsOgl7wblWNTVRHwY8AbQyVr7Wclra4HrgHDgGHCftXa5MeaXwATA\nAxwGllprf1LR+iubiNeUiibiItJwVDQRr0kVTcRFRESkYpVNxGuSJuLBI1gn4nXxjDjW2jcp9ZEI\n1tqby2n7J+BPTmyXiIiIiIiISG2rk4m4iIiIiIiIBLkgfsa6vquLD2sTERERERERabA0ERcRERER\nERFxkG5NFxERERERkTKC+eu/6jtdERcRERERERFxkCbiIiIiIiIiIg7SRFxERERERETEQXpGXERE\nRERERMrSM+K1RlfERURERERERBykibiIiIiIiIiIg1x5a/r9ZyMdyclo6kiMiAS5mb0nO5Izdsvj\njuQ4VY+IiMgFTtwBvSOsESuLjjiQBGmOpEh95sqJuIiIiIiIyOWcmoS7ib5HvPbo1nQRERERERER\nB2kiLiIiIiIiIuIgTcRFREREREREHKRnxEVERERERKQMPSNee3RFXERERERERMRBmoiLiIiIiIiI\nOEgTcREREREREREH6RlxERERERERKUPPiNceXREXERERERERcZAm4iIiIiIiIiIOanC3pseldKPb\n1HswoSHse3k1X8xa4rM8uu91dH98JC2vv5qNo//Mobc2VisnMakbAx4biQkNYccra/jwOd+c0CaN\nGDJjNHFdE8k/7mXxmFmcPHi0WlkDpoykXUoPzuYX8vb4uWTv2lemTVyXtqQ9cz+NwpqwZ/VHrJry\noupxoB4nctw2Nk7luGkfAPjN72bw3vsbiYpszaKX5pRZbq3liWfnsHb9JsLCmjJ90jiuv7ZDwDmg\n8QnmHNBxGqznULftA27rN7eNj1M5TvbbwCkjaV9Sz78rqGfIM/fTOKwJu1d/xMpq9NvPf/szbux/\nI4X5BTz10DNk7Moo0+aJF6cTFRtFaGgoOzfu4s+/mUVRke7hlsA4ekXcGLPGGHNrqdceNMbMNsYs\nM8bkGmPeKrX8ZWPM58aYXcaYvxtjGld7A0IM3Z+4l/d/8BQrbnmYrw/7FhHXfM2nSf5XR9n8qzkc\nePODaseYEMPAqen8M/0p5g+cQKehfYnumODTputdyRScOMW8pHFsnr+M5EdGVCurXUp3IhM9zEsa\nx/KJ80md9iO/7QZNv5flE+czL2kckYkeEpO7qZ5arseJHLeNjVM5btoHLrgzLZU5M6aVu3zt+k3s\nP3iIpa/OZ8qEXzL1D7OqlaPxCd4c0HEarOdQt+0Dbus3t42PUzl10W9/TRrHsonzubWcem6dfi/L\nJs7nryX1tAuw325MuYGvJX6N9JvvZeav/8ivfvcLv+2m/mw699/6M34ycBSto1txy+03B1pS/WFN\n/f8TpJy+Nf0fQOkjcETJ608DI/2852XgOqAr0Az4SXXDo3p24NTebE7vP4w9e56Di9YTf2tvnzan\nDxzl5KcH4Ar+VSu+R3ty92Vz4sARis6e59MlG+iQ6pvTMbUXuxauBeDzpRu5+qbO1crqkNqbjxeu\nAyBz227CWragRWxrnzYtYlvTJLwZh7YW/4vexwvX0XFQH9VTy/U4keO2sXEqx037wAV9enSlVcuI\ncpevXreBoYMHYIyhe5dOeL15HDmaE3COxid4c0DHabCeQ922D7it39w2Pk7lONlvHVN7s6uknkPb\ndtO0nHqaXlbPrmr027cGfZMVC1cC8Om2zwhv2YKo2Kgy7U7nnQYgtFEojRo3AhtwSSKOT8RfB243\nxjQFMMa0BRKAddbaVYC39BustUttCWAj8PXqhofFR5J/6NjFn/Mzc2gWX/bgulLhnki8mZd+wfVm\n5hDhiSzT5uSh4jb2fBGF3tM0iwwPOCvCE8nJy2ryZuUQEeebFREXiTer4u2piOqpXj1O5LhtbJzK\ncdM+UFXZR47hiY25+HNcbAzZRwK/PVDjE7w5oOM0WM+hbtsH3NZvbhsfp3Kc7jevA/0W44nhyKEj\nF38+knmUGE+037ZPvjSd17e9Sv6pfN7799qAckTA4Ym4tfYYxZPpwSUvjQBeLZlkV6jklvSRwLLq\n5hvj59aEyqMDz6FsTukYf9tSrU3xu54yYZW3qShC9fhtEww5bhsbp3LctA9Ulb/+8XtOrIzGJ2hz\nSlbkZz06Tusyw8mckhX5WU/9Gxs35pSsyM96dIxWHla9egIN878K/+t45O5J/Hef79O4SWN63NQj\noJz6xBbV/z/Bqi4+rO3C7en/Kvnvj6v4vtnAe9Zav//kZIwZBYwCuD/iBgY1L/shRPmHcmiWcOlf\ntZrFR5GfdTygja8Kb1YOEZddaY+IjyIv2zfHm5lDy4Qo8rJyMKEhNI1oTkFuXpXW3/OegXQbkQJA\n1o49tEyI5qsLWZ4o8g7nlt0eT+nt8W2jemquHidz3DI22geuLKcqPLExZB2+dAU8+/BRYmP8/yt/\naRqf4M7RcRq851Cnctw4Nm7K0TFavX7rdc9AupfUk7ljDxGX/Q5f1Xq8VahnaPodpH3/NgC+2P4F\nbRLaXFzWJj6GY9nlP8Z1tvAsH6xYz7cGfZOta7dWqS6RC+ri68sWAQOMMb2AZtbaSvdaY8xjQBvg\nofLaWGvnWmv7WGv7+JuEAxz/aDfh7Tw0v7oNpnEoX7/zm2S+s6WaZZQvc/seIhM9tLqqDSGNQ+l0\nR18yVviWmbFyK12GF3+ww7VpN7L/g0+qvP5tL6xkQdokFqRN4st3ttB5eD8A4nu2p9B7mlOlTkyn\nDudy5lQB8T3bA9B5eD8yVlS9btUTWD1O5rhlbLQPXFlOVST368viZauw1rJ916eEh7egTUzVHs3R\n+AR3jo7T4D2HOpXjxrFxU46O0er129YXVvJ82iSeL6mnS0k9CZXUk1BST5fh/fiyCvUsXrCE0YMf\nYPTgB3h/+QekDh8IQKee13HKe5qcw74T8bDmYRefGw8JDeEb/W/kQMaBKtclcoEJ+FaXmgg15jXg\nGmCRtXbKZa8nA+Ottbdf9tpPKL5qPsBam1+V9b/h+UG5RcUN6EG3x4u/ZuE//1jD53/8F50mfJfc\nj/aQ+c5WInu0o+/fx9K4dQvOF5yl8MgJViZN8LuujKbl/ztGu5Tu9J98NyY0hJ2vvcuGWYvp99Bw\nsnbsJWPlVkKbNmbIzNHEdW5LQW4ei8fM4sSBI+WuryIDp6aTmNSNc/lneHv8XLJ27gUgfel0FqRN\nAsDTNZHbnhlFo7Am7F2znZWTXwgoQ/VUrx4nctw2Nk7l1Md9YOyWx8vNefixJ9m0bQe5uSeJjmrN\nA/eN5Ny5cwDcNWwI1lqmz5jNug2baRYWxtRHx9Kl0zV+1zWz9+QKa9L4BG8O6DgN1nOo2/YBt/Wb\n28bHqZya7LfK7iBOnZpOu6RunM0/w9LL6rl36XSev6yeISX17FmznRWl6llZVPmY/WLaz7khuQ+F\n+YU8Pe4ZvtjxJQBzls1m9OAHaB3Tmun/8ziNmzQmJCSUjz74iNm/nUPRed8KVh5YHrwf1x2ArFuS\n6/1H0XneWxOUY1FXE/FhwBtAJ2vtZyWvraX409HDgWPAfdba5caYc8B/uPRBbm9Ya8v/bZSKJ+I1\nqaKJuIhITatoIl6TKpuIi4iI1DQnHuWtykS8xrJcMhHP7JdS7yfi8etWB+VY1MUz4lhr3wTfT3iw\n1vr9Aj5rbZ1so4iIiIiIiEht0CVdEREREREREQdpIi4iIiIiIiLiIN32LSIiIiIiImUE8/dw13e6\nIi4iIiIiIiLiIE3ERURERERERBykibiIiIiIiIiIg/SMuIiIiIiIiJRhbVB+Bbcr6Iq4iIiIiIiI\niIM0ERcRERERERFxkCbiIiIiIiIiIg5y5TPiH4ZZR3KinYkREQFgZu/JjuSM3fK4IzlO1SMiIsGv\nkQO/Vw8KaVP7IS6j7xGvPboiLiIiIiIiIuIgTcRFREREREREHOTKW9NFRERERETkytgifX1ZbdEV\ncREREREREREHaSIuIiIiIiIi4iBNxEVEREREREQcpGfERUREREREpAyrr2uuNboiLiIiIiIiIuIg\nTcRFREREREREHKSJuIiIiIiIiIiDGuQz4kMfS+falB6czT/Da+Of49DH+8q0uXX8f9PrO7fQrFUL\nJne+t1o5A6aMpF1KD87mF/L2+Llk7yqbE9elLWnP3E+jsCbsWf0Rq6a8GFBGYlI3Bjw2EhMawo5X\n1vDhc0t8loc2acSQGaOJ65pI/nEvi8fM4uTBo0Fbj3KCO8dNtTiV46Zj9De/m8F7728kKrI1i16a\nU2a5tZYnnp3D2vWbCAtryvRJ47j+2g7VqsVN/aZ6grsep2pxU5+5MUfjE9z91japG/2nFOfsfGUN\nG2eXzbltZnFOwXEvS34e/ONTX+h7xGuPo1fEjTFrjDG3lnrtQWPMbGPMMmNMrjHmrVLL5xtjthtj\ndhhjXjfGhF/JNlyb3IOYRA9PJ4/ljUfnMWz6fX7bfbpqK7O+/Ztq57RL6U5kood5SeNYPnE+qdN+\n5LfdoOn3snzifOYljSMy0UNicrcqZ5gQw8Cp6fwz/SnmD5xAp6F9ie6Y4NOm613JFJw4xbykcWye\nv4zkR0YEbT3KCe4cN9XiVI7bjtE701KZM2NaucvXrt/E/oOHWPrqfKZM+CVT/zAr0DIA9/Wb6gne\nepyqxU195sYcjU9w95sJMQycls7C9Kd4fsAErqsgZ/4t49j8t2XcMjG4x0cEnL81/R9A6SNjRMnr\nTwMj/bxnrLW2u7W2G7AfGHMlG9B5UG+2vLEWgP3bMmgW0ZyINq3LtNu/LQPvkdxq53RI7c3HC9cB\nkLltN2EtW9Ai1jenRWxrmoQ349DWDAA+XriOjoP6VDkjvkd7cvdlc+LAEYrOnufTJRvokNrbp03H\n1OhYMO8AACAASURBVF7sWlhc7+dLN3L1TZ2Dth7lBHeOm2pxKsdtx2ifHl1p1TKi3OWr121g6OAB\nGGPo3qUTXm8eR47mBFyL2/pN9QRvPU7V4qY+c2OOxie4+83Toz3H92VzYn9xzmdLNtB+kG9O+0G9\n+Pj14pwv6sH4iIDzE/HXgduNMU0BjDFtgQRgnbV2FeAt/QZr7cmStgZoBlzRh+i3jIvixKFjF38+\nkZVDS0/UlazSrwhPJCcvy/Fm5RARF+nbJi4Sb9alX1K9mTlEeHzbVCTcE4k3s+L3h3siOXmouI09\nX0Sh9zTNIgO/qcCJepQT3DluqsWpHDceoxXJPnIMT2zMxZ/jYmPIPhL4rYFu6zfVE7z1OFWLm/rM\njTkan+rlONVvEZ5IvIcu5eRl+qnlsjb2fBFngnx8RMDhZ8SttceMMRuBwcC/KL4a/qq1FX9DnTHm\neSAN+AQYd0UbYfw851AbX5DnJ6dMmVVpU1EE/t5fOqLyNlULq/16lBPkOW6qxaEcVx6jFfC3Ln/1\nVcZt/aZ6/LepWljt1uNULW7qMzfmaHyql+NYv1VlHfVtfOoRPSNee+riw9ou3J5+YSL+48reYK29\n1xgTCvwZuAt4vnQbY8woYBTAoKg+9Ii49AFB3xyZyo3f7w/Awe17aJUQfXFZK08UJ7OPX0E5l/S8\nZyDdRqQAkLVjDy0TovmqZFmEJ4q8w763unuzcoi47Gp8RHwUedlVvx3em5VDRHzp9/vW4s3MoWVC\nFHlZOZjQEJpGNKcgNy+o6lFO8Oa4qRYnc3ze74JjtKo8sTFkHb50BTz78FFiY6IreId/bus31RO8\n9dR2LU7luO0cqn1a/VZ6HREJl3LC46PIO1w2J+KynCZBOD4ipdXF15ctAgYYY3oBzay1W6vyJmvt\neeBVYHg5y+daa/tYa/tcPgkHWP/iCv6YNpE/pk3k43c20/s7NwNwdc8OFHhPX9Gz4Jfb9sJKFqRN\nYkHaJL58Zwudh/cDIL5newq9pzlV6kA+dTiXM6cKiO/ZHoDOw/uRsWJLlfMyt+8hMtFDq6vaENI4\nlE539CVjhW93ZqzcSpfhxfVem3Yj+z/4JOjqUU7w5ripFidzLnDLMVpVyf36snjZKqy1bN/1KeHh\nLWgTE/ijP27rN9UTvPXUdi1O5bjtHKp9Wv12uaxSOdfd0ZfdpXJ2r9hK5+8W51yTdiMHgnB8REoz\ndXErhTHmNeAaYJG1dsplrycD4621t5f8bID21tqMkv9/GsBaO76i9f+67fcrLOrbj9/LtUndOZNf\nyD8f/itf7dwDwK+WPsEf0yYCcNsjP6Dnt79V/BxI9nE2vrqalc8u9FlPtA2tsM6BU9NJTOrGufwz\nvD1+Llk79wKQvnQ6C9ImAeDpmshtz4yiUVgT9q7ZzsrJL1S4ztLapXSn/+S7i7/O4bV32TBrMf0e\nGk7Wjr1krNxKaNPGDJk5mrjObSnIzWPxmFmcOHAkoAwn61FOcOe4qRancurjMTp2y+N+1//wY0+y\nadsOcnNPEh3VmgfuG8m5c+cAuGvYEKy1TJ8xm3UbNtMsLIypj46lS6dryt3emb0nl7usPvZbRVRP\n8NbjVC1u6jM35mh86r7fQir47T0xpTspj91NSGgIO199lw9nLeamh4aTtXMvu1cU56Q9O5rYkpy3\nxszixP6yOVW5y7qm+m3Cf15yxT3d+3qk1vv77tt+tCIox6KuJuLDgDeATtbaz0peWwtcB4QDx4D7\ngBXAWqAlYIDtwM8ufIBbeSqbiNeUyibiIiL1UXkT8ZpW0URcREQaloom4jXFyced3TIR39u9/k/E\nE7cH50S8Lp4Rx1r7Jvh+woO19uZymt9U+1skIiIiIiIi4oy6eEZcREREREREpMHSRFxERERERETE\nQXVya7qIiIiIiIgEN32PeO3RFXERERERERERB2kiLiIiIiIiIuIg3ZouIiIiIiIiZVirW9Nri66I\ni4iIiIiIiDhIE3ERERERERERB2kiLiIiIiIiIuIgPSMuIiIiIiIiZdiiut4C99IVcREREREREREH\nufKKeAG2rjdBRKTemtl7siM5Y7c87kiOU/WIiEj1hTnw6/vL57+q/ZASExxLkvpKV8RFRERERESk\nwTLGDDbGfG6MyTDGPOJn+WhjzE5jzEfGmHXGmOuvNNOVV8RFRERERETkyhQ1gO8RN8aEAn8BUoGD\nwCZjzGJr7SeXNftfa+2ckvZDgRnA4CvJ1RVxERERERERaahuBDKstXustWeAV4BvX97AWnvysh9b\nwJU/C60r4iIiIiIiItJQfQ04cNnPB4FvlG5kjPk58BDQBOh/paG6Ii4iIiIiIiKuZIwZZYzZfNmf\nUaWb+HlbmSve1tq/WGvbA78GfnOl26Ur4iIiIiIiIlKGdcEz4tbaucDcCpocBK667OevA4cqaP8K\n8NyVbpeuiIuIiIiIiEhDtQnoaIxJNMY0AUYAiy9vYIzpeNmPQ4AvrzRUV8RFRERERESkQbLWnjPG\njAGWA6HA3621HxtjHgc2W2sXA2OMMQOBs8BxIP1KczURFxERERERkQbLWrsUWFrqtcmX/f+vajpT\nE3EREREREREpwxbV/2fEg1WDnIh/57F0rk/pydn8Ql4e/xwHP95Xps2Q8Xdxw3duoXmrFkzo/KOA\nMxKTujHgsZGY0BB2vLKGD59b4rM8tEkjhswYTVzXRPKPe1k8ZhYnDx6tVj0DpoykXUoPzuYX8vb4\nuWTvKltPXJe2pD1zP43CmrBn9UesmvJig67HqRy39Zub+kw5wZvzm9/N4L33NxIV2ZpFL80ps9xa\nyxPPzmHt+k2EhTVl+qRxXH9th4Brcaoe5ejcBu4bG7flgH7/CObxuTq5GzdPKc755B9r2DrbNyek\nSSNSnx1Nm66JFBz3svyBWXiruR+MffwXfKv/NyjIL2Dq2N/zxa6yjwLPfOn3RMdFExoayvaNO/jD\no3+kqKioWnnScDn6YW3GmDXGmFtLvfagMWa2MWaZMSbXGPNWOe/9szEm70q34frkHrRJjGda8oO8\n8ug8vjf9J37b7Vq1hRnfnlStDBNiGDg1nX+mP8X8gRPoNLQv0R0TfNp0vSuZghOnmJc0js3zl5H8\nyIhqZbVL6U5kood5SeNYPnE+qdN+5LfdoOn3snzifOYljSMy0UNicrcGW49TOW7rNzf1mXKCO+fO\ntFTmzJhW7vK16zex/+Ahlr46nykTfsnUP8wKOAPc129uywGd2wKlnODe15zKcdv4mBBD0rR0ltzz\nFP/bfwLXfLsvkaVyrh+RTGHuKV66eRzb/7aMbz1avf3gm/2/wVWJX+N7/e7myV8/w4QnxvptN2n0\nb7kn9Sf8sP+9tI5qTf/bk6qVJw2b05+a/g+KP4XuciNKXn8aGOnvTcaYPkDrmtiALoP6sOmN9wD4\nz7YMmkU0p2Wbsqv+z7YMTh7JrVZGfI/25O7L5sSBIxSdPc+nSzbQIbW3T5uOqb3YtXAtAJ8v3cjV\nN3WuVlaH1N58vHAdAJnbdhPWsgUtYn3raRHbmibhzTi0NQOAjxeuo+OgPg22Hqdy3NZvbuoz5QR3\nTp8eXWnVMqLc5avXbWDo4AEYY+jepRNebx5HjuYEnOO2fnNbDujcFijlBPe+5lSO28Ynrkd7TuzL\n5uT+4pwvF2+g3SDfnHaDevHZ68U5Gf/eyNeruR/ccutNvP36OwB8vPVTwlu1IDo2qky703mnAQht\nFErjJo3KfuG0SBU4PRF/HbjdGNMUwBjTFkgA1llrVwHe0m8wxoRSPEmfUBMb0DouitxDxy7+fCIr\nh1aesgfYlQj3ROLNvPRLoTczhwhPZJk2Jw8Vt7Hniyj0nqZZZHjAWRGeSE5eVo83K4eION+siLhI\nvFkVb09F3FaPUzlu6zc39ZlygjunMtlHjuGJjbn4c1xsDNlHAr8F0W395rYc0LktWMfGbTmg3z+C\neXxaeCLxHrqUk5eZQ4tSOZe3seeLOOM9TVg19oM2nhiyDx2++PORzKO08cT4bTvz5adYuv1NTufl\ns/qtdwPOqi+srf9/gpWjE3Fr7TFgIzC45KURwKvWVthFY4DF1trMGtkIP583UHF8dSLKhpSOMKby\nNlUL87eeMmGVt6kowmX1OJXjun5zUZ8pJ7hzKuNvn/KXWxm39ZvbckpW5Gc9Orcpp2ZzSlbkZz36\n/SMYcvz1R9lL0FVpU72s8rZ37A8ncEev4TRu0pjeN/WsRpg0dHXxYW0Xbk//V8l/f1xeQ2NMAvA9\nILmylRpjRgGjAPpH9aFLRPuLy/qNHMQ3v98fgP3bd9M6IfrislaeKE5mH69GGeXzZuUQEX/pKntE\nfBR5pTK8mTm0TIgiLysHExpC04jmFORW7RH4nvcMpNuIFACyduyhZUI0X13I8kSRd9j3lnpvVg4R\nntLbU/Xb7t1Sj/ot8Hrc1mfKqR85lfHExpB1+NIV8OzDR4mNia7gHf65rd/ckqNzW/COjdty9PtH\ncI/PBacyc4hIuJQTHh/FqVI5p7KK25wqyWkSQM7w9DsZ+sMhAHz60WfEJcReXNYmPoaj2eXfcXWm\n8CzrVnzALbfexKa1WwIpS8TxW9MBFgEDjDG9gGbW2q0VtO0JdAAyjDH7gObGmAx/Da21c621fay1\nfS6fhAOse/Ednk57hKfTHmHnO5u54Tu3APBfPTtQ4D1d7WfBy5O5fQ+RiR5aXdWGkMahdLqjLxkr\nfMvMWLmVLsNvBuDatBvZ/8EnVV7/thdWsiBtEgvSJvHlO1voPLwfAPE921PoPc2pUif0U4dzOXOq\ngPiexf3SeXg/MlZU/WThlnrUb4HX47Y+U079yKlMcr++LF62Cmst23d9Snh4C9rEBP6Ikdv6zS05\nOrcF79i4LUe/fwT3+FyQvX0Prdp6iCjJ6Ti0L3tL5exdsZXrvluc02HIjRx8v+o5CxcsIn3QT0kf\n9FPeW/4+t313EACde3Xi1MlTHDvs+xkkzZqHXXxuPDQ0hG/2/wb/ydgfcF31hS0y9f5PsDI1fVt2\nlUKNeQ24BlhkrZ1y2evJwHhr7e3lvC/PWlvpAx+/ajuiwqK++/i9dErqwZn8Qv734Tkc2LkHgIeX\nPsnTaY8AMPSRH9D72zfRMi6Sk9nHWf/qapY9+7rPer5my7+hoF1Kd/pPvhsTGsLO195lw6zF9Hto\nOFk79pKxciuhTRszZOZo4jq3pSA3j8VjZnHiwJHKSvNr4NR0EpO6cS7/DG+Pn0vWzr0ApC+dzoK0\n4k9+93RN5LZnRtEorAl712xn5eQXAspwWz1O5bit39zUZ8qp+5yxWx73+/rDjz3Jpm07yM09SXRU\nax64byTnzp0D4K5hQ7DWMn3GbNZt2EyzsDCmPjqWLp2uKXebZ/ae7Eg9FVGOzm1uGxu35YB+/6jr\n8Wlewbd//VdKd26eUpzzyavvsuXPi7lx3HAO79jLvhXFOanPjiamS1sKc/NY/vNZnNxfNufloq/8\nrN3X+Om/4hvJN1CYX8i0h37PZzu+AGDBO/NIH/RTImMi+cOC39GkSWNCQkPZ8v5W/jjlL5w/71vA\n+q9WB+8MMACftB8SxE9ZV831u/8dlGNRVxPxYcAbQCdr7Wclr60FrgPCgWPAfdba5aXeVyMT8ZpS\n0URcREQqVt5EvKZVNBEXEZHgUNFEvKZUZSJeUzQRDx7BOhGvk5mktfZNSn2qgrX25iq8r2Y/eldE\nRERERETEYbqkKyIiIiIiImUU2aC8mOwKdfFhbSIiIiIiIiINlibiIiIiIiIiIg7SRFxERERERETE\nQXpGXERERERERMqweka81uiKuIiIiIiIiIiDNBEXERERERERcZAm4iIiIiIiIiIO0jPiIiIiIiIi\nUoa1db0F7qUr4iIiIiIiIiIOcuUV8cO2wJGcrxHuSI6IiBvN7D3ZkZyxWx53JMepekRE3Oi0A5cH\nh4V8rfZDRKpIV8RFREREREREHOTKK+IiIiIiIiJyZYr0PeK1RlfERURERERERBykibiIiIiIiIiI\ng3RruoiIiIiIiJRhdWt6rdEVcREREREREREHaSIuIiIiIiIi4iBNxEVEREREREQcpGfERURERERE\npAxr63oL3EtXxEVEREREREQc1CCviKdP+Qk9UnpzJr+Q58b/iX279vgsbxLWhAefm0Ds1R5sURFb\nVm7ild+/GHDOgCkjaZfSg7P5hbw9fi7Zu/aVaRPXpS1pz9xPo7Am7Fn9EaumBJaTmNSNAY+NxISG\nsOOVNXz43BKf5aFNGjFkxmjiuiaSf9zL4jGzOHnwaMC1OJUDzvSbm3K0D+jYcTIH3DM+v/ndDN57\nfyNRka1Z9NKcMsuttTzx7BzWrt9EWFhTpk8ax/XXdggow8l63JgDOodWh1uOUTfW47Z+U071c0Qc\nvSJujFljjLm11GsPGmNmG2OWGWNyjTFvlVr+P8aYvcaYj0r+9LiSbeiR0htPYjxjk37GvImzuW/a\naL/t3pq7iPEDxvBI2kNc26cT3ZN7BZTTLqU7kYke5iWNY/nE+aRO+5HfdoOm38vyifOZlzSOyEQP\nicndqpxhQgwDp6bzz/SnmD9wAp2G9iW6Y4JPm653JVNw4hTzksaxef4ykh8ZEVAdTuaAM/3mphzt\nAzp2nMwBd43PnWmpzJkxrdzla9dvYv/BQyx9dT5TJvySqX+YFXAGuG8/cNP+pj4L7mPUbfW4rd+U\nU/0cEXD+1vR/AKXPKCNKXn8aGFnO+x621vYo+fPRlWxA79QbWbtwDQAZ276gecsWtI6N9GlzpuAM\nn6zfBcD5s+fYu2s30Z7ogHI6pPbm44XrAMjctpuwli1oEdvap02L2NY0CW/Goa0ZAHy8cB0dB/Wp\nckZ8j/bk7svmxIEjFJ09z6dLNtAhtbdPm46pvdi1cC0Any/dyNU3dQ6oDidzwJl+c1OO9gEdO07m\ngLvGp0+PrrRqGVHu8tXrNjB08ACMMXTv0gmvN48jR3MCznHbfuCm/U19FtzHqNvqcVu/Kaf6OfVJ\nkTX1/k+wcnoi/jpwuzGmKYAxpi2QAKyz1q4CvLW9AVGeKI4dunSLT07WMaLiospt37xlC3oNvIFd\n7+8IKCfCE8nJQ8cu/uzNyiEiznfCHxEXiTfr0i913swcIjy+bSoS7onEm1nx+8M9kZw8VNzGni+i\n0HuaZpHhAdXiVA44029uytE+UP72VMRt/abxqX49Fck+cgxPbMzFn+NiY8g+Evgtom7bD9y0v6nP\nyt+eiqge7QfKubIcEXB4Im6tPQZsBAaXvDQCeNXaSj+Pb7oxZocxZuaFSXx1GVP2X0XKSw8JDeEX\nf36I5c//m8MHsgMN8pNjA29TUQSV1xJIvXWdU7IiP+up2X5zU472gQraVBThsn7T+Phvc6X8bbO/\n3Mq4bT9w0/6mPqugTUURqsdvm2DJKVmRn/XUv99zXJsjQt18WNuF29P/VfLfH1fSfiKQBTQB5gK/\nBh4v3cgYMwoYBdAnqjsdwtteXJZ6z230HzEIgD07viQ64dIVjihPNMcP+7/V8KdPPkDW3kze/vsS\nv8tL63nPQLqNSAEga8ceWiZE81XJsghPFHmHc33ae7NyiPBcuhofER9FXrZvm4p4s3KIiC/9/uO+\nbTJzaJkQRV5WDiY0hKYRzSnIzatyhhM5TvWb23Iuvlf7QIM9dpzKcev4VMYTG0PW4UtXwLMPHyU2\nJrDHlMA9+4FTOTqHBnefqR7tB8q5sv1N5IK6+PqyRcAAY0wvoJm1dmtFja21mbZYIfA8cGM57eZa\na/tYa/tcPgkHWPHC20xMG8vEtLFsfudDbh6eDECHntdw2nuK3MPHy6zvv8f/gGYRLXjht/OrXNi2\nF1ayIG0SC9Im8eU7W+g8vB8A8T3bU+g9zalSB/Kpw7mcOVVAfM/2AHQe3o+MFVuqnJe5fQ+RiR5a\nXdWGkMahdLqjLxkrfLszY+VWugy/GYBr025k/wefVHn9TuU41W9uywHtAw392HEqx63jU5nkfn1Z\nvGwV1lq27/qU8PAWtIkp/1Gm8rhlP3AqR+fQ4O4z1aP9QDlXtr/VN9aaev8nWJm6uJXCGPMacA2w\nyFo75bLXk4Hx1trbL3st3lqbaYrv1ZkJFFhrH6lo/d//rzsrLOreqaPontSLwvxC/jr+T+zZuRuA\nJ5bOZGLaWKI80fzlw/l8lXGAs4XnAHjnhX+z+pWVPuvpScXP7Aycmk5iUjfO5Z/h7fFzydq5F4D0\npdNZkDYJAE/XRG57ZhSNwpqwd812Vk5+ocJ1ltYupTv9J9+NCQ1h52vvsmHWYvo9NJysHXvJWLmV\n0KaNGTJzNHGd21KQm8fiMbM4ceBIQBlO5oAz/eamHO0DOnaczIH6Nz5jt5S5iQqAhx97kk3bdpCb\ne5LoqNY8cN9Izp0rPuffNWwI1lqmz5jNug2baRYWxtRHx9Kl0zXlbvPM3pMdqacibssBnUOro74d\now2pHrf1m3LKz5nwn5eCdwYYgE1fG1bv77u/4as3g3Is6moiPgx4A+hkrf2s5LW1wHVAOHAMuM9a\nu9wY839AG8AAHwGjrbUV3qNT2US8plQ2ERcRkbpX3kS8plU0ERcRkYZFE/HgEawT8bp4Rhxr7Zvg\n+4kV1tqby2nb35GNEhEREREREXFAnUzERUREREREJLgF8/dw13d18WFtIiIiIiIiIg2WJuIiIiIi\nIiIiDtJEXERERERERMRBekZcREREREREyqj3H5kexHRFXERERERERMRBmoiLiIiIiIiIOEi3pouI\niIiIiEgZ+vqy2qMr4iIiIiIiIiIO0kRcRERERERExEGaiIuIiIiIiIg4yJXPiPex4Y7knNcjEyIi\nQW9m78mO5Izd8nitZzhVi4iIG23kZF1vQr1j9Yx4rdEVcREREREREREHaSIuIiIiIiIi4iBNxEVE\nREREREQc5MpnxEVEREREROTKFNX1BriYroiLiIiIiIiIOEgTcREREREREREHaSIuIiIiIiIi4iA9\nIy4iIiIiIiJlWPQ94rVFV8RFREREREREHKSJuIiIiIiIiIiDGtyt6W2TupEyZSQmNIRdr6xh4+wl\nPstDmzTitpmjie2aSMFxL2/9fBYnDx4NOCcxqRsDHivO2fHKGj58rmzOkBmjieuaSP5xL4vHKOeC\nAVNG0i6lB2fzC3l7/Fyyd+0r0yauS1vSnrmfRmFN2LP6I1ZNebHB5mgfCN6xcTLHbfuBm3J+87sZ\nvPf+RqIiW7PopTlllltreeLZOaxdv4mwsKZMnzSO66/tEHAtTtVzgVv2a/VZ8B47F7jpXO1UjvaD\n6o/Pj6f8lJ4pfTiTX8is8c+yd9cen+VNwpow7rlf47k6nqKiIjav3MjLv3+hWjVJw+boFXFjzBpj\nzK2lXnvQGDPbGLPMGJNrjHmr1HJjjJlujPnCGPOpMeaX1c4PMQyYls4b6U/xPwMmcO3QvkR1TPBp\n0+WuZApOnOLvt4xjy9+WccvEEdXKGTg1nX+mP8X8gRPoNLQv0aVyupbkzEsax+b5y0h+RDkA7VK6\nE5noYV7SOJZPnE/qtB/5bTdo+r0snzifeUnjiEz0kJjcrUHmaB8I3rFxMsdt+4Hbcu5MS2XOjGnl\nLl+7fhP7Dx5i6avzmTLhl0z9w6yAM8B9x6kT9ajPgvvYAXedq53K0X5Q/fHpmdKb+MQEfpF0P3Mm\n/oVR037mt93iuYv41YAHeDjtQa7r04meyb0CLaneKLL1/0+wcvrW9H8ApY/AESWvPw2M9POeHwFX\nAddZazsBr1Q33NOjPbn7sjmx/whFZ8/z+ZINdBjU26dNh0G9+Pj1tQB8sXQjV9/UOeCc+As5B4pz\nPl2ygQ6pvjkdU3uxa2FxzufKuahDam8+XrgOgMxtuwlr2YIWsa192rSIbU2T8GYc2poBwMcL19Fx\nUJ8GmaN9IHjHxskct+0Hbsvp06MrrVpGlLt89boNDB08AGMM3bt0wuvN48jRnIBz3HacOlGP+iy4\njx1w17naqRztB9UfnxtSv8GahasB+HLb5zRv2YLWsZE+bc4UnOHj9TsBOHf2HHt27SbaE1OtuqRh\nc3oi/jpwuzGmKYAxpi2QAKyz1q4CvH7e8zPgcWttEYC19nB1w8M9kXgPXfrlxpuZQ3hcZLlt7Pki\nCr2naRYZHnhOpm9OhKdszknllBHhieTkoWOXsrJyiCg1RhFxkXizKt6ehpKjfaD87WlIOW7bD9yW\nU5nsI8fwxF76JS4uNobsI4Hfuum249SJetRnwX/suOlc7VSO9oPyt6cy0Z5ojh06cvHnnKxjRMdF\nl9u+ecsW9Bl4Izve3x5Qjgg4PBG31h4DNgKDS14aAbxqra3opoH2wF3GmM3GmLeNMR2rm2+Mn4/f\nL53sp02FW+cvx8/H/Jdeh79taeg5JSvys54yYZW3aSA52gcqaNOActy2H7gtpzL+xtrv31eVcNtx\n6kQ96jP/bYIlp2RFftZTP8/VTuVoP6igTaU5ZV8qbx0hoSGM/fN4lj7/FocPZAeWI0LdfFjbhdvT\n/1Xy3x9X0r4pUGCt7WOM+Q7wd+Dm0o2MMaOAUQDfjbyRvuFl5+vezBwiEqIu/hwRH0Xe4eM+bfJK\n2uRl5WBCQ2ga0ZyC3LxA6iv+V7r4UjnZvjnezBxaKgeAnvcMpNuIFACyduyhZUI0X13I8kSRdzi3\n7PZ4Sm+Pb5uGkHPxvdoHgm5snNwHLr7fBfuBW3Mq44mNIevwpSvg2YePEhtT/hWY8rjlOHWqHicy\n3NhnTuS47Vyt/SC494PB96QxYMQgAHbv+JLohDbApwBEeaLJOez/UaHRT44hc+8h/v33xVWqp74q\n0veI15q6+PqyRcAAY0wvoJm1dmsl7Q8CC0v+/03A76cuWGvnWmv7WGv7+JuEA2Rt30PrRA8tr2pD\nSONQrr2jL7tX+MbvXrGVzt8tnudfk3Yj+z/4pMqFXZC5fQ+RiR5aleR0uqMvGaVyMlZupcvwuxvB\nCAAAIABJREFU4pxrG3jOthdWsiBtEgvSJvHlO1voPLwfAPE921PoPc2pUifaU4dzOXOqgPie7QHo\nPLwfGSu2NLgc0D4QrGPj5D4A7tkP3JpTmeR+fVm8bBXWWrbv+pTw8Ba0iYmq/I2luOU4daoeJzLc\n2GdO5LjtXK39ILj3g2UvLOXhtAd5OO1BNr7zIcnDiyf/HXtey2nvaXJLXbQDGDH+hzSPaM7zv/1b\nlesRKc0EfMtGTYQa8xpwDbDIWjvlsteTgfHW2tsve+1J4Atr7d9Llj9trb2hovU/c/Xd5RaVmNKd\n5MfuJiQ0hF2vvsuHsxbzrYeGk71zL7tXbCW0aWNue3Y0sZ3bUpCbx7/HzOLE/iN+13W+gn8gapfS\nnf6T78aEhrDztXfZMGsx/R4aTtaOvWSsLM4ZMnM0cSU5i8fM4sQB/zkVcVsOwMCp6SQmdeNc/hne\nHj+XrJ17AUhfOp0FaZMA8HRN5LZnRtEorAl712xn5eTAvzbCLTnaB4J3bJzMcdt+UB9zxm553O/r\nDz/2JJu27SA39yTRUa154L6RnDt3DoC7hg3BWsv0GbNZt2EzzcLCmProWLp0usbvumb2nuxYPZVx\ny36tPqv7Y6cybjpXO5Wj/cB/v23kZKU5P5l6Pz2SelGYX8js8X9i987iD397eumzPJz2IFGeaOZ+\n+DwHMw5wtvAsAMte+DerXlnhs57X/7PYFZeS/y/uv4P4c8erpn/2a0E5FnU1ER8GvAF0stZ+VvLa\nWuA6IBw4BtxnrV1ujGkNvAxcDeQBo621FX4iQkUT8ZpU0URcREQalvIm4jWpsom4iIiUryoT8Zri\nlon4qri76v1EfED2q0E5FnXxjDjW2jcp9XEI1toyz32XvJ4LDHFiu0RERERERERqW108Iy4iIiIi\nIiLSYGkiLiIiIiIiIuKgOrk1XURERERERIJbUV1vgIvpiriIiIiIiIiIgzQRFxEREREREXGQJuIi\nIiIiIiIiDtIz4iIiIiIiIlKGJSi/gtsVdEVcRERERERExEGaiIuIiIiIiIg4SBNxEREREREREQe5\n8hnx7JDzjuTE2FBHckREJPjN7D251jPGbnm81jPAmVpERC4Xams/Y9/Z47Uf4jL6HvHaoyviIiIi\nIiIiIg7SRFxERERERETEQZqIi4iIiIiIiDjIlc+Ii4iIiIiIyJXRM+K1R1fERURERERERBykibiI\niIiIiIiIg3RruoiIiIiIiJRhMXW9Ca6lK+IiIiIiIiIiDtJEXERERERERMRBmoiLiIiIiIiIOEjP\niIuIiIiIiEgZRXpEvNY0yIn40MfSuS6lB2fzz/Da+Of46uN9ZdrcOv6/6f2dW2jWqgX/r/O91coZ\nMGUk7VJ6cDa/kLfHzyV7V9mcuC5tSXvmfhqFNWHP6o9YNeXFoMxJTOrGgMdGYkJD2PHKGj58bonP\n8tAmjRgyYzRxXRPJP+5l8ZhZnDx4NOBa3FaPEzkam+AdGydzQOecYB4fJ3J+87sZvPf+RqIiW7Po\npTlllltreeLZOaxdv4mwsKZMnzSO66/tEHAtF7hpf3NTLU7luO3c5qZzgZM54Mz4tE3qRsqU4np2\nvbKGjbPL1nPbzNHEdk2k4LiXt35e/XrGT/0VNw3oS0F+IVMe/B2f7/yiTJs//e8fiImNJrRRKB99\nuJ3fT5xJUZG+cVsC4+it6caYNcaYW0u99qAxZrYxZpkxJtcY81ap5WuNMR+V/DlkjFl0JdtwXXIP\nYhI9PJU8loWPzmPY9Pv8tvt01Vb+/O3fVDunXUp3IhM9zEsax/KJ80md9iO/7QZNv5flE+czL2kc\nkYkeEpO7BV2OCTEMnJrOP9OfYv7ACXQa2pfojgk+bbrelUzBiVPMSxrH5vnLSH5kREB1uLEeJ3I0\nNsE7Nk7mgM45wTw+TuXcmZbKnBnTyl2+dv0m9h88xNJX5zNlwi+Z+odZAWdc4Kb9zU21OJXjtnOb\n284FbhyfAdPSeSP9Kf5nwASuHdqXqFL1dCmp5++3jGPL35Zxy8Tq1XNT/75c1e7rDPvW95n+8FNM\nfHKc33YTR03mBwPv5a7ke4iMbs3AO1KqlScNm9PPiP8DKH1kjCh5/WlgZOk3WGtvttb2sNb2ANYD\nb1zJBlw/qDdb31gLwP5tGTSLaE5Em9Zl2u3floH3SG61czqk9ubjhesAyNy2m7CWLWgR65vTIrY1\nTcKbcWhrBgAfL1xHx0F9gi4nvkd7cvdlc+LAEYrOnufTJRvokNrbp03H1F7sWljcr58v3cjVN3UO\nqA431uNEjsYmeMfGyRzQOSeYx8epnD49utKqZUS5y1ev28DQwQMwxtC9Sye83jyOHM0JOAfctb+5\nqRanctx2bnPbucBt4+O5UM/+4no+X7KBDoN86+kwqBcfv15czxdXUE/S4H4s/ecyAHZt/YSIluFE\nx0aXaXcq7zQAoY1CadS4MdbaauVJw+b0RPx14HZjTFMAY0xbIAFYZ61dBXjLe6MxJgLoD1zRFfFW\ncVHkHjp28efcrBxaeaKuZJV+RXgiOXlZjjcrh4i4SN82cZF4sy79EuTNzCHC49smGHLCPZF4Myt+\nf7gnkpOHitvY80UUek/TLDI8oFrAXfU4kaOxCd6xcTIHdM4J5vFxcj+oSPaRY3hiYy7+HBcbQ/aR\n6t266ab9zU21OJXjtnOb284FrhyfQ77vD48rW4+3Bupp42lD1qHDF3/OzjxCbHyM37Z//sczrNi5\nhNN5p1n11pqAs+qLIky9/xOsHJ2IW2uPARuBwSUvjQBetVX7Z6RhwCpr7ckr2ghTdjBq5V+xqpJT\nE9viQI7xswOXjai8TdXC3FOPEzkaG/9tGlpOyYr8rEfnnIaUUxl/Y+Avt0pctL+5qhaHctx2bnPb\nucB14+PvPFX67TVUj/9+8b+iX3x/HIN73EmTpo25oV+vwMOkwauLD2u7cHv6v0r+++Mqvu/7wN/K\nW2iMGQWMAhgU1YfuEZc+gOabI1P5xvf7A3Bg+x5aJ1y6xaS1J4qT2ccDq6AcPe8ZSLcRxc+IZO3Y\nQ8uEaL4qWRbhiSLvsO+t7t6sHCIuuxofER9FXnblt8M7lePz/vjS7/ftM29mDi0TosjLysGEhtA0\nojkFuXlVWr/b6nEyR2MTvGPjRI7OOcE9Pk7nVMYTG0PW4UtXwLMPHyU2puwtl+Vx0/7mplqczPF5\nvwvObU7V47Ycx8cnM4eIhFLvP+xbT15Jm+rU870fDePOH94BwCfbP8OTEMv2kmVx8W04knWs3Pee\nKTzDu8vfJ+nWfnz43uYq1yQCdfM94ouAAcaYXkAza+3Wyt5gjIkGbgT+XV4ba+1ca20fa22fyyfh\nAOv/P3t3Hl9Vde99/LMSCIEkkBMgOaHVEiZFZqGUq2CCDNqgotKn4K0Yh/tYrvW2RZAK+AAXxKkV\neK6pUpHbB6FVrCgNVwTBioKCDEEQRGsEBMzAEEJOQhiznj9yApknkp3D5vt+vXhpzl5nf/dae+0N\nv+y9z1m8hnmJk5mXOJnd72/l+rsHAXB1n04U+E5e0rPgJW1/bS2LEqeyKHEq37y/jW6jBgIQ26cj\np30nyS9zYso/nMOZ/FPE9ukIQLdRA0lbsy1gcopl7NiLJ85Lq6vaEtQ0mK63DyBtTendlrY2le6j\nisb1msT+HPj0yxqv3239cTJH+yZw940TOTrnBPb+cTqnOgkDB5Cy6gOstezYtYfw8DDatqn5o1lu\nmm9u6ouTOcXccm5zqj9uy3F6/2Tu2EtknJeW/v5cc/sAvi3Tn2/XpNLtZ0X96VLL/vzt/73DL4Y9\nyC+GPci699aT+L+Kbtztfv115PnyOHa4dCHevEXzC8+NBwcHc+OQAexPO1DjPJFipjE+XMAY8ybQ\nBVhurZ1R4vUEYKK19rYy7ccB/2KtTarJ+ie1v6fKTt058wGuie/FmYLT/O3xP3Hoi70A/HblM8xL\nnAxA4hP/Su+RN9AyxkNu1nG2LP2QNfOWlVpPGxtc5XYMnZVEXHxPzhWc4b2Jr5D5xT4AklbOZlHi\nVAC8PeL46QsP0yQ0hH3rdrB22ms16aLjOR0G9+LmafdigoP44s2P2JScwsDHRpG5cx9pa1MJbtaU\nEXPHEdOtPady8kh5NJkTB4/Uui9u648TOdo3gbtvnMwBnXMCef/UV874bTMrzXh8+rNs2b6TnJxc\nWkdF8shDYzl37hwAo+8agbWW2XNeYsOmrTQPDWXWlPF079qlwnXN7Tut2j65ab65qS9O5bjt3Ha5\nnQsCJQfqb/8EV/Gv97jBvUiYfi9BwUHsWvoRnyWncMNjo8j6Yh/frinqz0/njSPa3593H03mxIHy\n/Xn97HfV9mfS0+O5YfBPOFVwiv8c/wx7dnwNwF/W/De/GPYgUW08zF38HCEhIQQFB7F1Qypzpr/I\n+fPnS61na8b6wH04uRaWe//1sv8kujsz/xqQ+6KxCvG7KPr0867W2q/8r60HrgXCgWPAQ9ba1f5l\n64BnrbWrarL+6grx+lJdIS4iIlKfqirE61NNCnERkfpUVSFeX2pSiNcXFeKBI1AL8cZ4Rhxr7TtQ\n+pMkrLWDqmif0NDbJCIiIiIiIuKExnhGXEREREREROSK1ShXxEVERERERCSwFTb2BriYroiLiIiI\niIiIOEiFuIiIiIiIiIiDVIiLiIiIiIiIOEjPiIuIiIiIiEg5hSYgv/nLFXRFXERERERERMRBKsRF\nREREREREHKRb00VERERERKQc29gb4GK6Ii4iIiIiIiLiIFdeEe95JtiRnPSmjsSIiIgAMLfvNEdy\nxm+b6UiOU/0RkcDXzIFLrwOatWv4EJEa0hVxEREREREREQe58oq4iIiIiIiIXJrCxt4AF9MVcRER\nEREREREHqRAXERERERERcZAKcREREREREREH6RlxERERERERKafQNPYWuJeuiIuIiIiIiIg4SIW4\niIiIiIiIiINUiIuIiIiIiIg4SM+Ii4iIiIiISDmF6CHxhnLFFeKxCT358ayxmKAg0l5fx+7kFaWW\nR//kGvrNHEtk16vY8O/JHHh3S51y4uJ7MmT6WExwEDvfWMdnL5fOCQ5pwog544jpEUfBcR8pjyaT\ne+iocpRTpxyAITPG0mFwb84WnOa9ia+QtWt/uTYx3duT+MIvaRIawt4PP+eDGYsDLsNt+8Ztc8Bt\n4+a2/eNEzpNPz+HjTzYT5Ylk+ZL55ZZba3lm3nzWb9xCaGgzZk+dwHXXdKp1P5zqj1MZylEOuO/c\n5lTO1Qk9GTSjKOfL19eR+lLpnKCQJgybN462PeI4ddzH6keS8dXxXP2z6ffTbXAfzhScZvHElzm0\ne1+5NrdPHE3/u2+iRatwJnRLqlOOiKO3phtj1hljbinz2m+NMS8ZY1YZY3KMMf9TZvkQY0yqMeZz\nY8wGY0yd/zY3QYb+Tyfxj188z4qESbQfOYBWnduVapP//TE+/e2f2P/Op3WNwQQZhs5K4m9Jz7Nw\n6CS63jGA1mVyeoxO4NSJfBbET2DrwlUkPDFGOcqpUw5Ah8G98MR5WRA/gdWTFzLsqfsrbDd89gOs\nnryQBfET8MR5iUvoGVAZbts3bpsDbhs3t+0fp3LuTBzG/DlPVbp8/cYtHDiUzsqlC5kx6dfM+kNy\nrfpQklvObcpRDrjv3OZkTvxTSay473n+evMkuowcgKdMznVjEjidk8+SQRPY8eoqbphSt3P1dQm9\naRvn5T8TfsPrUxYwZvZDFbb74oNUfj9yap0yRIo5/Yz460DZI2OM//XfA2MreM/LwC+stb2BvwJP\n1jW8dZ+O+PZnkXfgCIVnz7P/75v44S19S7XJP3SUnD0HsYW2rjHE9u5Izv4sThwsytmzYhOdhpXO\n6TzsenYtWw/A1ys3c/WN3ZSjnDrlAHQa1pfdyzYAkLH9W0JbhhEWHVmqTVh0JCHhzUlPTQNg97IN\ndB7eL6Ay3LZv3DYH3DZubts/TuX0692DVi0jKl3+4YZN3HHrEIwx9OreFZ8vjyNHs2vVj2JuObcp\nRzngvnObUzkxvTtyYn8Wuf5/v3+TsokOw0vndBh+PV+9VZST9u5mfljHc3XP4T9m89sfA7B/+zc0\njwijZdvIcu32b/+G3CM5dcoQKeZ0If4WcJsxphmAMaY90A7YYK39APBV8B4LtPT/fysgva7hLbwe\nTqZf/MfAyYxsWsR66rq6SoV7PfgyLub4MrKJ8HrKtcn1b4s9X8hp30mae8KVo5xa5wBEeD3kph+7\nmJWZTURM6ayIGA++zKq3p7Ez3LZv3DYH3DZubts/TuZUJevIMbzRbS78HBPdhqwjdbtF1C3nNuUo\nB9x3bnMqJ8zrwVfi3+95GdmElckp2caeL+SM7yShdThXR8Z4OF5iHuRkHiPSG1Xr9biJdcGfQOXo\nM+LW2mPGmM3ArcDfKboavtRaW9UY/Ruw0hhTAOQCA+q8Aab8hw1UmVzXmAo+1KBsjqmHbVGOckqs\nqIL1lAurvk0jZ7ht37htDrht3Ny2fxzNqUJF66poHGvEJec25SgH3Hduc+wcWtH5o9w6atKmbln1\neX4UKakxPqyt+Pb04kL8wWrajwcSrbWfGWMeB+ZQVJyXYox5GHgY4IFW/bm5RedyKzqZkU2Ldhd/\nq9UiNoqCzON17EblfJnZRMRezImIjSIvq3SOLyOblu2iyMvMxgQH0SyiBady8pSjnBrn9LlvKD3H\nDAYgc+deWrZrzffFWd4o8g6XvmXKl5lNhLfs9lR9W5UTGeXe74J941SO9k9g5zi1f5yeB9XxRrch\n8/DFK+BZh48S3aZ1jd/vpnObcpRT7v0uOLc5nZOfkU1EiX+/h8dGkV8mJz+zqE2+PyekFjk3jR3O\nDfcMAeC7Hd/iaXfxfBXpbc2JrPqvFUSgcb5HfDkwxBhzPdDcWptaWUNjTFugl7X2M/9LS4EbKmpr\nrX3FWtvPWtuvoiIc4Njne4mI8xJ2VVuCmgbTfuQADr1faXydZezYiyfOSyt/TtfbB5C2pnRO2tpU\nuo8aBMA1if058OmXylFOrXK2v7aWRYlTWZQ4lW/e30a3UQMBiO3TkdO+k+SX+QdE/uEczuSfIrZP\nRwC6jRpI2pptjZ5Rklv2jVM52j+BnePU/nF6HlQnYeAAUlZ9gLWWHbv2EB4eRts2Nb+1003nNuUo\npyS3nNuczsnasZdW7b1E+HM63zGAfWVy9q1J5dqfFeV0GtGfQ5/UPOfjxe/zbOLveDbxd+x8fwv9\n774JgPZ9OlPgO3nFPwteaC7/P4HKNMbtFsaYN4EuwHJr7YwSrycAE621t/l/bgJkAjdYa/9pjHmI\noqvjo6pa/5J291baqXY396Lff96LCQ7i2zc+Ytd/pdDz8VFk79jHofdTad2rAzct/C3NIltw/tRZ\nCo6c4H8GP1HhutKbVr4NHQb34uZpRTlfvPkRm5JTGPjYKDJ37iNtbSrBzZoyYu44Yrq151ROHimP\nJnPi4JGquqUc5VRp6Kwk4uJ7cq7gDO9NfIXML4q+biNp5WwWJRZ9sqe3Rxw/feFhmoSGsG/dDtZO\ney3gMty2b9w2B9w2bm7bP/WVM37bzErX//j0Z9myfSc5Obm0jorkkYfGcu7cOQBG3zUCay2z57zE\nhk1baR4ayqwp4+netUuF65rbd5oj/QmEDOUoB9x3bqvPnBaFlef8aHAvBs0oyvly6UdsezGF/hNG\ncXjnPvavKcoZNm8cbbq353ROHqt/lUzugfI5XwWfrbZPP5/5IF3je3G24AxLHn+ZA1/sBeCJlc/x\nbOLvABj5xC/oN/JGWsV4OJF1nI1L/8HKeW+VWk/y/qUBXALW3Gs/qLyuulzc9/2SgNwXjVWI3wW8\nDXS11n7lf209cC0QDhwDHrLWrva3nQkUAseBB621e6taf1WFeH2qqhAXERG5XFVViNenmhTiInJl\nqKoQry81KcTriwrxwBGohXhjPCOOtfYdynyqgrV2UBVt33Fiu0REREREREQaWqMU4iIiIiIiIhLY\nHLhR4YrVGB/WJiIiIiIiInLFUiEuIiIiIiIi4iAV4iIiIiIiIiIO0jPiIiIiIiIiUs5l/5HpAUxX\nxEVEREREREQcpEJcRERERERExEEqxEVEREREREQcpGfERUREREREpJxC09hb4F66Ii4iIiIiIiLi\nIFdeET8e3NhbICIicvma23eaIznjt810JMep/ohI3WUEFzZ4xrXnmzZ4hkhN6Yq4iIiIiIiIiINc\neUVcRERERERELk3D36dw5dIVcREREREREREHqRAXERERERERcZAKcREREREREREH6RlxERERERER\nKUfPiDccXREXERERERERcZAKcREREREREREH6dZ0ERERERERKceaxt4C99IVcREREREREREHqRAX\nERERERERcdAVd2v61Qk9uWnGWExwEF++vo5tL60otTwopAnD542jbY84Th33seqRZHyHjtY6Jy6+\nJ0OmF+XsfGMdn71cOic4pAkj5owjpkccBcd9pDyaTK5yABgyYywdBvfmbMFp3pv4Clm79pdrE9O9\nPYkv/JImoSHs/fBzPpixOCBz3DZubhoz5QT2XHNbjvZP7XOefHoOH3+ymShPJMuXzC+33FrLM/Pm\ns37jFkJDmzF76gSuu6ZTrfuhYzSw57RyAnseOJkzYvp9XDO4N2cLzrBs4nzSd5fPGTbx5/S+exDN\nW4Uxs9uDtc64OqEng0rUCakV1AnDStQJq+tYJ4iAw1fEjTHrjDG3lHntt8aYl4wxq4wxOcaY/ymz\n/GZjTKoxZpcxZpExps6/PDBBhoSnkki573n+cvMkuowcgKdzu1Jtuo1J4FROPosHTeDzV1dx45Qx\ndcoZOiuJvyU9z8Khk+h6xwBal8npMTqBUyfyWRA/ga0LV5HwhHIAOgzuhSfOy4L4CayevJBhT91f\nYbvhsx9g9eSFLIifgCfOS1xCz4DLcdu4uWnMlBPYc81tOdo/dcu5M3EY8+c8Veny9Ru3cOBQOiuX\nLmTGpF8z6w/JteoD6BgN9DmtnMCeB07mdEnoTZs4L3MSHmP5lFe5Y3bFRfZXH6Qyf+T/qdW6i5kg\nQ/xTSay473n+WkmdcN2YBE7n5LNk0AR2vLqKG+pQJ1xuCl3wJ1A5fWv660DZGTvG//rvgbElFxhj\ngoBFwBhrbXfgOyCpruExvTuSsz+L3ANHKDx7nn+mbKLD8L6l2sQNv56v3loPQNq7m/nhjd1qnRPr\nzzlxsChnz4pNdBpWOqfzsOvZtawo5+uVm7laOQB0GtaX3cs2AJCx/VtCW4YRFh1Zqk1YdCQh4c1J\nT00DYPeyDXQe3i/gctw2bm4aM+UE9lxzW472T91y+vXuQauWEZUu/3DDJu64dQjGGHp174rPl8eR\no9m16oeO0cCe08oJ7HngZE7X4X3Z/nbRuBzcnkZoRAsi2kaWa3dwexq+Izm1WnexmN4dOVGiTvim\ngjqhQz3UCSLFnC7E3wJuM8Y0AzDGtAfaARustR8AvjLtWwOnrbX/9P+8BhhV1/Awr4e89It/Sedl\nZBPu9ZRqE+714PO3secLOeM7SagnvFY54V4PvoyLOb6MbCIqyMktkXPad5LmV3gOQITXQ276sYtZ\nmdlExJTOiojx4MusensCIcdt4+amMVNOYM81t+Vo/9Q9pypZR47hjW5z4eeY6DZkHandLaI6Rivf\nnqq4bdzclgPuOxe0jPFwosS/4XMzs2lZj+cTKKoTfGXqhLAyGSXb1LVOECnmaCFurT0GbAZu9b80\nBlhqrbWVvOUo0NQYU/xrs58BV9U135jyn79fPrmCz+ivbOsqy6lgHWVzarYtV1aOf0UVrKdcWPVt\nAiDHdePmojFTTt1y/CuqYD2X5zHqVI72zyXkVKGidVU0jlXRMVpFm6oiXDZubsvxr6iC9Vy+54J6\nG5eqQ8q/1gB1gkixxviwtuLb0//u/2+ln6RgrbXGmDHAXP9V9PeBcxW1NcY8DDwMMDqyPzeGdy7X\nJi8jm/B2URd+Do+NIj/reOk2mdlEtIsiPzMbExxESEQLTuXk1aqDvsxsImIv5kTERpFXJseXkU3L\ndlHk+XOaXcE5fe4bSs8xgwHI3LmXlu1a831xljeKvMOlbzHyZWYT4S27PdXfhuRUTqn3X+bj5rYx\nU07gzjU35pR6v/ZPrcetOt7oNmQevngFPOvwUaLbtK7VOnSMltyewJnTygnseeBUzk/GDuPH9xTl\nHNqxl1Yl/g3f0huFr8zYXar8jKIaoFhFdUJ+PdQJl5tAfsb6ctcYX1+2HBhijLkeaG6tTa2qsbV2\no7V2kLW2P/Ax8E0l7V6x1vaz1varqAgHyNqxl8j2Xlpe1ZagpsF0uWMA+9aUjt+3JpVrfzYIgE4j\n+nPoky9r3cGMHXvxxHlp5c/pevsA0srkpK1NpfuoopxrEvtz4NMrN2f7a2tZlDiVRYlT+eb9bXQb\nNRCA2D4dOe07SX6ZE3r+4RzO5J8itk9HALqNGkjamm0Bk1PMDePmtjFTTuDONTfmFNP+qdu4VSdh\n4ABSVn2AtZYdu/YQHh5G2zZR1b+xBB2jgTmnlRPY88CpnM8WryE5cQrJiVPY8/5W+txdNC5X9enE\naV9BnZ8Fr0zWjr20au8lwr9/OjdQnSBSzNTnbWI1DjXmTaALsNxaO6PE6wnARGvtbSVei7bWHvZf\nEV8JzLbW/qOq9b941b2VdupHg3sxaMa9BAUH8eXSj9j6Ygo/mTCKwzv3sW9NKsHNmhZ9LUH39pzO\nyWPVr5LJPXCkwnUVVPFrjA6De3HztHsxwUF88eZHbEpOYeBjo8jcuY+0tUU5I+aOI6Zbe07l5JHy\naDInDlacUxW35QAMnZVEXHxPzhWc4b2Jr5D5xT4AklbOZlHiVAC8PeL46QsP0yQ0hH3rdrB22msB\nmeO2cXPTmCknsOea23K0fyrOGb9tZqXrf3z6s2zZvpOcnFxaR0XyyENjOXeu6Ka40Xe9XR9kAAAg\nAElEQVSNwFrL7DkvsWHTVpqHhjJryni6d+1S4brm9p1WaY6O0cCe08oJ7HlQnzknTNXXXm+feT+d\n43txtuA0bz/+J7735zy68mmSE6cAcMsT99Br5A1Fz6VnHWfr0nX8Y96yC+uIPV/1NcjiOsH464Rt\nL6bQ318n7C9RJ7Tx1wmrq6gTHj24pHbPygSo5CrqqstFoO6LxirE7wLeBrpaa7/yv7YeuBYIB44B\nD1lrVxtjfg/cRtHV+5ettfOqW39VhXh9qqoQFxERkapVVYjXp6oKcREJDNUV4vWhukK8PgVq8Vdb\nKsQbTmM8I4619h3KfNqBtXZQJW0fBx53YrtERERERESkyGVfhQcwXdMVERERERERcZAKcRERERER\nEREHqRAXERERERERcVCjPCMuIiIiIiIiga0wID/mzB10RVxERERERETEQSrERURERERERBykQlxE\nRERERETEQXpGXERERERERMopbOwNcDFdERcRERERERFxkApxEREREREREQe58tb0rGBnbqJoafV7\nDBERkbqa23eaIznjt810JMep/oi40cNRhxs8Y/lRb4NnuI1uTW84qiRFREREREREHKRCXERERERE\nRMRBKsRFREREREREHKRCXERERERERMqxLvhTE8aYW40xXxtj0owxT1SwvJkxZql/+WfGmPY1XHWl\nVIiLiIiIiIjIFckYEwz8EfgpcB1wjzHmujLNHgKOW2s7AXOB5y41V4W4iIiIiIiIXKn6A2nW2r3W\n2jPAG8DIMm1GAov8//8WMMQYYy4lVIW4iIiIiIiIXKl+ABws8fMh/2sVtrHWngNOAK0vJdSV3yMu\nIiIiIiIil6bwkq75BgZjzMPAwyVeesVa+0rJJhW8rezj5TVpUysqxEVERERERMSV/EX3K1U0OQRc\nVeLnHwLplbQ5ZIxpArQCsi9lu3RruoiIiIiIiFyptgCdjTFxxpgQYAyQUqZNCpDk//+fAf+w1uqK\neG2NmH4fXQb35mzBGZZNnE/G7v3l2gyd+HP63D2I0FZhzOr2YK0z4uJ7MmT6WExwEDvfWMdnL68o\ntTw4pAkj5owjpkccBcd9pDyaTO6ho3Xqz5AZY+kwuDdnC07z3sRXyNpVvj8x3duT+MIvaRIawt4P\nP+eDGYuv6Byn9o8TOZprygF3zeliGrfAHTencpwYtyefnsPHn2wmyhPJ8iXzyy231vLMvPms37iF\n0NBmzJ46geuu6VSrjGJuGTM35oC7jh235YTe8GOiJj4CwUHkvfMeuf/vjVLLw0fdRsTPR0LheQpP\nniL7qTmc3Xeg1n35UXxP4mcUzbfdb6xj60vl59vwueOI7hHHqeM+Vv4qGV8d55sEDmvtOWPMo8Bq\nIBj4b2vtbmPMTGCrtTYFWAgsNsakUXQlfMyl5jp6RdwYs84Yc0uZ135rjFlpjNlojNltjNlpjBld\nYnmc/7vavvF/d1vIpWxDl4TetI7zMjfhMZZPeZU7ZldcZH/1QSovj/w/dcowQYahs5L4W9LzLBw6\nia53DKB153al2vQYncCpE/ksiJ/A1oWrSHiibvuyw+BeeOK8LIifwOrJCxn21P0Vths++wFWT17I\ngvgJeOK8xCX0vGJznNo/TuRorikH3DWni2ncAnfcnMpxatzuTBzG/DlPVbp8/cYtHDiUzsqlC5kx\n6dfM+kNyrTPAXWPmthxw17HjupygIKJ+9x8c/o8ppI96iLBbB9M07upSTfJX/YOM0f+bjHvGkbto\nKZ4J/16rfkDRfEt4KonlSc+zeMgkutwxgKgy863b6AROn8hn0U0T2P7qKgZOvuRaLOAVuuBPTVhr\nV1pru1hrO1prZ/tfm+YvwrHWnrLW/i9rbSdrbX9r7d4aD2IlnL41/XXK//ZgDEXfw3aftbYbcCsw\nzxgT6V/+HDDXWtsZOE7Rd7jVWdfhffn87fUAHNqeRmhEC8LbRpZrd2h7GnlHcuqUEdu7Izn7szhx\n8AiFZ8+zZ8UmOg3rW6pN52HXs2tZ0XZ8vXIzV9/YrU5ZnYb1ZfeyDQBkbP+W0JZhhEWX7k9YdCQh\n4c1JT00DYPeyDXQe3u+KzXFq/ziRo7mmHHDXnC6mcQvccXMqx6lx69e7B61aRlS6/MMNm7jj1iEY\nY+jVvSs+Xx5Hjtb+sUA3jZnbcsBdx47bckK6X8O5Q+mc+z4Dzp0jf/U6mifcWKqNzT954f9N81Co\nwx3DMb07cmJ/FrkHiubbP1dsosPw0vOtw/Dr+fKtovn2zcrNXFXH+SYCzhfibwG3GWOaARhj2gPt\ngI+ttd8AWGvTgcNAW/93s93sfx8UfXfbnZeyARExHk6kX/wLNDczm5Zez6WsspxwrwdfxsUMX0Y2\nEWUywr0ecv3bYc8Xctp3kuae8FpnRXg95KYfu5iVmU1ETOmsiBgPvsyqt+dKynFq/ziRo7mmHHDX\nnC6mcQvccXMqx8lxq0rWkWN4o9tc+Dkmug1ZR2p/K6qbxsxtOeCuY8dtOU3atuFc5uELP58/fITg\n6PLfGhX+8zto9/fX8Pzmf5P9/B9r042i93s9+ErUCHkZ2YSX6UuY10NemfkWWs/nHLlyOFqIW2uP\nAZspuuoNRVfDl5Z80N0Y0x8IAb6l6LvZcvzf1QYVf6dbrVT4veuX9Jh9BRkVfLp92V/MVbQddXrc\nv8L1lAurvs0VlOPU/nEiR3NNOeCuOV1iRRWsR+NWfZjm9aV9dE55FW1zhf+WqI6LxsxtOf4VVbCe\ny/PYcV1ODf/tnvdmCukj7+P4f71Kq3/7Rc3XX0VOTeZbfdcRcuVojA9rK749/e/+/154SNsYEwss\nBpKstYWm4r/pKpzuJb8f7qdRP+b6iIsfpPKTscPod89gAL7fsZdW7aIuLGvpjSI36/gldqk0X2Y2\nEbEXMyJio8grk+HLyKZluyjyMrMxwUE0i2jBqZy8Gq2/z31D6TmmqD+ZO/fSsl1rvi/O8kaRd7j0\nLfW+zGwivGW3p/rb7t2WU+r9Dbh/nMzRXLuyc0q93wVzWuMW2OPm1v1THW90GzIPX7wCnnX4KNFt\nyl+Nq4hbx8wtOW47dtyWU+zc4SM08UZf+Dk4ui3njxyrtP3J1R/SevJvqLxFxfIysokoUSOEx0aR\nf/h4uTbhDXzOCTT6PUPDaYyvL1sODDHGXA80t9amAhhjWgLvAk9aazf52x4FIv3f1QYVf6cbUPT9\ncNbaftbafiWLcIDPFq/hj4lT+GPiFL58fyu97x5UtLI+nTjtK6jzs+CVydixF0+cl1ZXtSWoaTBd\nbx9A2prUUm3S1qbSfVTRdlyT2J8Dn35Z4/Vvf20tixKnsihxKt+8v41uowYCENunI6d9J8kvcwLM\nP5zDmfxTxPbpCEC3UQNJW7Ptissp1tD7x8kczbUrO6eYW+a0xi2wx82t+6c6CQMHkLLqA6y17Ni1\nh/DwMNq2iar+jbh3zNyS47Zjx205xc7s/pomV/2AJu280KQJYbckUPDRp6XaNLnq4g2zzQf9hLMH\nD9V4/cWyduwlMs5LS/9863L7APaWmW9716Ry3c+K5lvnxP4cbIBzjlw5zCV+/VndQo15E+gCLLfW\nzvB/Evp7wApr7bwybf8GLLPWvmGMmQ/stNa+VNX6n2z/r1V26raZ99MlvhdnCk7z9uN/Iv2LfQD8\nauXT/DFxCgC3PHEPPUfeUPRcS9Zxti1dxz/mLSu1npa28t9jdBjci5un3YsJDuKLNz9iU3IKAx8b\nRebOfaStTSW4WVNGzB1HTLf2nMrJI+XRZE4cPFLVZldq6Kwk4uJ7cq7gDO9NfIVMf3+SVs5mUeJU\nALw94vjpCw/TJDSEfet2sHbaa1d0jlP7x4kczTXlgLvmdDGNW+COm1M59TVu47fNrDTj8enPsmX7\nTnJycmkdFckjD43l3LmiJ+JG3zUCay2z57zEhk1baR4ayqwp4+netUuF65rbd1qV/bmcxuxKywF3\nHTuXY87o1pmVZoTe2L/o68uCgshLWUXuwr/SalwSZ778JwUfb8Qz8RFCf3I9nDtHYW4e2c+9yNm9\n35Vbz/Kj3ir70n5wL26aXjTfvlz6EVuSUxjw2CiyvtjHvjVF8+2WeeNo659v7z2aTO6Biufbbw4s\nqcMzLIHnmR/de9lfFJ/8XWDui8YqxO8C3ga6Wmu/MsbcC/wZ2F2i2f3W2s+NMR2AN4AoYDtwr7X2\ndFXrr64Qry9VFeIiIiISGKoqxOtTdYW4iFSuqkK8vlRXiNcntxTis3/0i8u+EJ/63V8Ccl80xjPi\nWGvfgYufwGGtXQIsqaTtXqC/Q5smIiIiIiIi0qB0SVdERERERETEQSrERURERERERBzUKLemi4iI\niIiISGArbOwNcDFdERcRERERERFxkApxEREREREREQepEBcRERERERFxkJ4RFxERERERkXIu+y8R\nD2C6Ii4iIiIiIiLiIBXiIiIiIiIiIg5SIS4iIiIiIiLiID0jLiIiIiIiIuXoe8QbjisL8R+ed+ZC\nf67uJxAREQl4c/tOcyRn/LaZjuQ41R8RJ23KiGnwjKaurHzkcqVSUkRERERERMRBKsRFRERERERE\nHKQbNERERERERKScQtPYW+BeuiIuIiIiIiIi4iAV4iIiIiIiIiIOUiEuIiIiIiIi4iA9Iy4iIiIi\nIiLlFGIbexNcS1fERURERERERBykQlxERERERETEQbo1XURERERERMrRjekN54orxK9K6MmNM8Zi\ngoPY8/o6Pn9pRanlQSFNuHneONr2iOPUcR9rH0nGd+honbKGzBhLh8G9OVtwmvcmvkLWrv3l2sR0\nb0/iC7+kSWgIez/8nA9mLK5VRlx8T4ZML+rPzjfW8dnLpfsTHNKEEXPGEdMjjoLjPlIeTSY3gPuj\nnNrnuG0OONUfjVtgj5vbcsA95xwnc5zaP0705cmn5/DxJ5uJ8kSyfMn8csuttTwzbz7rN24hNLQZ\ns6dO4LprOtW6L247dnSMBnaOU/vHO7gnfWYW5ez96zq+Si6d03bAtfSZeS+tul7NxnHJHHp3c60z\noKhOGDhjLEHBQXz5+jq2V1AnDC1RJ7x/CXWCiKO3phtj1hljbinz2m+NMSuNMRuNMbuNMTuNMaNL\nLH/UGJNmjLHGmDaXlB9kGPhUEu/e9zxLb55Ep5ED8HRuV6pN1zEJnM7J5/VBE9j56ip+MmVMnbI6\nDO6FJ87LgvgJrJ68kGFP3V9hu+GzH2D15IUsiJ+AJ85LXELPWvVn6Kwk/pb0PAuHTqLrHQNoXaY/\nPUYncOpEPgviJ7B14SoSngjc/iin9jlumwNO9UfjFtjj5rYccM85x8kcp/aPU2N2Z+Iw5s95qtLl\n6zdu4cChdFYuXciMSb9m1h+Sa7V+cN+xo2M0sHOcnAd9n76fj3/xPKviJ/GjO/+Fll1+UKpN/qGj\nfPabP3HgnU9rvf6SOTf564TXb55E5yrqhL8MmsCOV1fxL3WsE0TA+WfEXwfKztgxwHPAfdbabsCt\nwDxjTKR/+SfAUOC7Sw2P7t2R3P1Z+A4cofDseb5N2UT74X1LtWk//Hr++dZ6APa+u5kf3NitTlmd\nhvVl97INAGRs/5bQlmGERUeWahMWHUlIeHPSU9MA2L1sA52H96txRmzvjuTsz+LEwaL+7FmxiU7D\nSven87Dr2bWsqD9fr9zM1QHcH+XUPsdtc8Cp/mjcAnvc3JYD7jnnOJnj1P5xasz69e5Bq5YRlS7/\ncMMm7rh1CMYYenXvis+Xx5Gj2bXKcNuxo2M0sHOc2j9RfTri259Fvv/f7wf+vokf3FI65+Sho5zY\ncxBbWPcbqaN7d+TE/ixy/TlpKZuIK1MnxA2/nq/8dcK3l1AniIDzhfhbwG3GmGYAxpj2QDvgY2vt\nNwDW2nTgMNDW//N2a+3++ggP83rIS7/4l1peRjZhXk+lbez5Qs74ThLqCa91VoTXQ276sQs/+zKz\niYgpnRUR48GXeXF7fBnZRJTZnqqEez34Mqp+f7jXQ26J/pz2naR5gPZHObXPcdsccKo/GrfAHje3\n5YB7zjlO5ji1f5was+pkHTmGN/rijX8x0W3IOlK7W17dduzoGA3sHKf2T3NvFAXfX+zLyYxsmtfz\n8QfO1gmXk0IX/AlUjhbi1tpjwGaKrnpD0dXwpdbaC7++Msb0B0KAb+t9A4ypYJvKNapBm7pm2dq3\nqSqiBttqatTnmoQ1fH+UU/sct80Bp/qjcau4jXIu73ngthzH9o9TY1aNitZXUf+q4rZjR8doYOc4\nd4xW8Fo9H39Qs22tSZ9FaqoxPqyt+Pb0v/v/+2DxAmNMLLAYSLLW1uoXGMaYh4GHAf41sj+DwjuX\na5OfkU14u6gLP4fHRnEy63jpNplFbfIzszHBQYREtOB0Tl6NtqHPfUPpOWYwAJk799KyXWu+9y+L\n8EaRdzinVHtfZjYR3ovbExEbRV5W6TZV8WVmExFb9v2l++PLyKZluyjy/P1pFtGCUwHWH+XUfR64\nZQ441R+ncjRuygH3nXPcNK+d7ktNeKPbkHn44hXwrMNHiW7TulbrcMux41SO244dNx2jJRVkZNP8\nBxePhRaxURTU8/EHRVfAq6sT8i6hThApqzG+R3w5MMQYcz3Q3FqbCmCMaQm8Czxprd1U25Vaa1+x\n1vaz1varqAgHOLxjL63ae4m4qi1BTYPpeMcA9q9JLdVm/5pUuvxsEAAdRvQn/ZMva7wN219by6LE\nqSxKnMo372+j26iBAMT26chp30nyy5wA8w/ncCb/FLF9OgLQbdRA0tZsq3Fexo69eOK8tPL3p+vt\nA0gr05+0tal0H1XUn2sS+3Pg08Drj3LqPg/cMgec6o9TORo35YD7zjlumtdO96UmEgYOIGXVB1hr\n2bFrD+HhYbRtE1X9G0twy7HjVI7bjh03HaMlZX++l4g4L2H+nKtHDuD71fV7/EH5OqHTHQPYV0Gd\ncK2/Tug4oj/f16JOECnL1PetVTUKNeZNoAuw3Fo7wxgTArwHrLDWzqvkPfuBftbaah+Ymn/VvZV2\n6urBvbhhxr2Y4CC+XvoRqS+m0G/CKI7s3Md3a1IJbtaUm+eNo0339pzOyWPNr5LxHThS4bpyq/k1\nxtBZScTF9+RcwRnem/gKmV/sAyBp5WwWJU4FwNsjjp++8DBNQkPYt24Ha6e9Vl33SukwuBc3Tyvq\nzxdvfsSm5BQGPjaKzJ37SFtb1J8Rc8cR0609p3LySHk0mRMHK+5PdZzoj3Jqn+O2OeBUfzRugT1u\nbssB95xznMxxav/UV1/Gb5tZacbj059ly/ad5OTk0joqkkceGsu5c+cAGH3XCKy1zJ7zEhs2baV5\naCizpoyne9cuFa5rbt9plea47djRMRrYOfW5f350pvKaJPbmXhe/vuyNj9jzf/9O98dHkb1jH+nv\npxLVqwM3/vd4QiJbcP7UWU4dOcGqhN+VW8+xJlU/7nH14F4M9NcJXy39iG0vpvBjf52w318nDJk3\njrbdi/qz5lfJ5FZSJzxycEntni0JUL9rf89lf/P9c/tfD8h90ViF+F3A20BXa+1Xxph7gT8Du0s0\nu99a+7kx5tfAJMBL0Ye4rbTW/ltV66+qEK9P1RXiIiIicuWoqhCvT1UV4iKXq6oK8fpSXSFen1SI\nB45ALcQb4xlxrLXvUOKjF6y1S4AllbT9L+C/HNo0ERERERERkQala7oiIiIiIiIiDmqUK+IiIiIi\nIiIS2C77+9IDmK6Ii4iIiIiIiDhIhbiIiIiIiIiIg1SIi4iIiIiIiDhIz4iLiIiIiIhIOYWNvQEu\npiviIiIiIiIiIg5SIS4iIiIiIiLiIBXiIiIiIiIiIg7SM+IiIiIiIiJSTqG+SbzBuLIQ/8HZ847k\n5DYLdiRHREREAt/cvtMcyRm/baYjOU71RwTg82YNX/B5rGnwDJGa0q3pIiIiIiIiIg5y5RVxERER\nERERuTS6Mb3h6Iq4iIiIiIiIiINUiIuIiIiIiIg4SIW4iIiIiIiIiIP0jLiIiIiIiIiUU9jYG+Bi\nuiIuIiIiIiIi4iAV4iIiIiIiIiIOUiEuIiIiIiIi4iA9Iy4iIiIiIiLlWH2TeIO54grxtoN70X3W\nfZjgIA785UPSklNKLY8acC3dZ95HxHVXkzruv8j4n811yomL78mQ6WMxwUHsfGMdn728otTy4JAm\njJgzjpgecRQc95HyaDK5h44qx2U5AENmjKXD4N6cLTjNexNfIWvX/nJtYrq3J/GFX9IkNIS9H37O\nBzMWB1yO9k3g7hs35rhtHritP27Kcdu57cmn5/DxJ5uJ8kSyfMn8csuttTwzbz7rN24hNLQZs6dO\n4LprOgVsf3TsKKfY7dPv45rBvTlTcIa3Js4nfXf5nOETf06fuwfRvFUYM7o9WOsMJ+ebiKO3phtj\n1hljbinz2m+NMSuNMRuNMbuNMTuNMaNLLP+LMeZrY8wuY8x/G2Oa1nkDggw9nnmAz/71OT68aSLt\n7rqB8C4/KNWk4PujbP/NfL5/55M6x5ggw9BZSfwt6XkWDp1E1zsG0Lpzu1JteoxO4NSJfBbET2Dr\nwlUkPDFGOS7LAegwuBeeOC8L4iewevJChj11f4Xths9+gNWTF7IgfgKeOC9xCT0DKkf7JnD3jRtz\n3DYP3NYfN+W48dx2Z+Iw5s95qtLl6zdu4cChdFYuXciMSb9m1h+Sa53hxnFzy5x2a841Cb1pHefl\nDwmP8c6UV7lzdsVF9p4PUnlp5P+p1bqLOTnfRMD5Z8RfB8rO2DHAc8B91tpuwK3APGNMpH/5X4Br\ngR5Ac+Df6hru6dOJ/H2ZnDxwGHv2POnLN+K9pV+pNgUHj+LbcwAK634bRmzvjuTsz+LEwSMUnj3P\nnhWb6DSsb6k2nYddz65l6wH4euVmrr6xm3JclgPQaVhfdi/bAEDG9m8JbRlGWHRkqTZh0ZGEhDcn\nPTUNgN3LNtB5eL9y62rMHO2bwN03bsxx2zxwW3/clOPGc1u/3j1o1TKi0uUfbtjEHbcOwRhDr+5d\n8fnyOHI0u1YZbhw3t8xpt+Z0Hd6X7W8X7eeD29MIjWhBRNvIcu0Obk/DdySnVusu5uR8EwHnC/G3\ngNuMMc0AjDHtgXbAx9babwCstenAYaCt/+eV1g/YDPywruGhsR4K0o9d+PlUxjFCYz11XV2lwr0e\nfBkX/1LzZWQT4fWUa5ObXtTGni/ktO8kzT3hynFRDkCE10NuiTnny8wmIqZ0VkSMB19m1dvT2Dna\nN5Vvj3LqP8dt88Bt/XFTjhvPbdXJOnIMb3SbCz/HRLch60jtbq1147i5ZU67NadVjIec9IvrOJGZ\nTctarqM6gXScBpJCF/wJVI4W4tbaYxQV07f6XxoDLPUX2QAYY/oDIcC3Jd/rvyV9LLCqzhtgTAUb\nVee1VR5D+RxbJsdUsC1l2yjn8s7xr6iC9ZQLq75NI+do31TRRjn1nuO2eeC2/rgpx5XntmpUNDYV\nZVfFlePmkjl9ZeXUbhXVRgTQcSpXhsb4sLbi29P/7v/vhYc8jDGxwGIgyVpb9hcYL1F05Xx9RSs1\nxjwMPAzwSEQ/bm1R/oNHTqVn07xd6ws/h8a25lTm8UvqTEV8mdlExEZd+DkiNoq8rNI5voxsWraL\nIi8zGxMcRLOIFpzKyVOOC3L63DeUnmMGA5C5cy8t27Xm++IsbxR5h0vfMuXLzCbCW3Z7qr+tyqmc\nC+/Vvgm4feO2nFLvd8E8cFt/3JZz4b0uOLfVhje6DZmHL14Bzzp8lOg2rat4R3luGTe3zWm35QwY\nO4wf31OUc2jHXiLbRfGdf1krbxS+rPr9N3wgHadyZWiM7xFfDgwxxlwPNLfWpgIYY1oC7wJPWms3\nlXyDMWY6RbeqP1bZSq21r1hr+1lr+1VUhAPkfP4tYR28NL+6LaZpMO3u/Bcy399WT926KGPHXjxx\nXlpd1ZagpsF0vX0AaWtSS7VJW5tK91GDALgmsT8HPv1SOS7J2f7aWhYlTmVR4lS+eX8b3UYNBCC2\nT0dO+06SX+YvqPzDOZzJP0Vsn44AdBs1kLQ11c9Lp3JA+yZQ943bcoq5ZR64rT9uywH3nNtqI2Hg\nAFJWfYC1lh279hAeHkbbNlHVv7EEt4yb2+a023I2LV7Di4lTeDFxCl++v5U+dxft56v6dOKUr6DO\nz4JXJpCOU7kymFrfGlIfoca8CXQBlltrZxhjQoD3gBXW2nll2v4bRVfNh1hrC2qy/hXeeyrtVPSQ\n3nSbWfT1ZQdfX8c3/3c510z6GTmf7yPr/W206t2BH//3YzSNDKPw1FlOHznBuvjHK1zXnmbBlW5D\nh8G9uHnavZjgIL548yM2Jacw8LFRZO7cR9raVIKbNWXE3HHEdGvPqZw8Uh5N5sTBIzXpnnIuoxyA\nobOSiIvvybmCM7w38RUyv9gHQNLK2SxKnAqAt0ccP33hYZqEhrBv3Q7WTnst4HK0bwJ337gxx23z\nwG39cVPO5XhuG79tZqU5j09/li3bd5KTk0vrqEgeeWgs586dA2D0XSOw1jJ7zkts2LSV5qGhzJoy\nnu5du1S4rrl9pznSn6ro2Llyco6bqp/mvWPm/XSJ78XZgtO89fif+N6f8x8rn+bFxCkA3PrEPfQe\neUPRc+lZx9mydB0fzFt2YR0eW/U1yPqcb5O+W1K7Zz4C1CPtf37Z33z/0v43A3JfNFYhfhfwNtDV\nWvuVMeZe4M/A7hLN7rfWfm6MOQd8B/j8r79tra38byCqLsTrU1WFuIiIiEhDqKoQr09VFeIi9a26\nQrw+VFeI1ycV4oEjUAvxxnhGHGvtO3DxExGstUuAJZW0bZRtFBEREREREWkIKnJFRERERESknMv+\ncngAa4wPaxMRERERERG5YqkQFxEREREREXGQCnERERERERERB+kZcRERERERESmnUE+JNxhdERcR\nERERERFxkApxEREREREREQepEBcRERERERFxkJ4RFxERERERkXIKG3sDXExXxEeGsO4AACAASURB\nVEVEREREREQcpEJcRERERERExEGuvDU9vUlwY2+CiIiISIOY23eaIznjt810JMep/khg83G+wTM8\nugYpAcSVhbiIiIiIiIhcGqvvEW8w+rWQiIiIiIiIiINUiIuIiIiIiIg4SIW4iIiIiIiIiIP0jLiI\niIiIiIiUo+8Rbzi6Ii4iIiIiIiLiIBXiIiIiIiIiIg5SIS4iIiIiIiLiID0jLiIiIiIiIuXoe8Qb\njq6Ii4iIiIiIiDjoirsiflVCT274z7GY4CC+en0dn/9xRanlsT+5hn+ZMZbWXa9i7a+S2ffuljrl\nxMX3ZMj0opydb6zjs5dL5wSHNGHEnHHE9Iij4LiPlEeTyT10VDkuywEYMmMsHQb35mzBad6b+ApZ\nu/aXaxPTvT2JL/ySJqEh7P3wcz6YsTjg+qIc5RRr6DntZH+Uo3ngtn3jVM6TT8/h4082E+WJZPmS\n+eWWW2t5Zt581m/cQmhoM2ZPncB113SqdQ44M9ecynHbPHDynPOz6ffTbXAfzhScZvHElzm0e1+5\nNrdPHE3/u2+iRatwJnRLqlOOU/NNxNEr4saYdcaYW8q89ltjzEpjzEZjzG5jzE5jzOgSyxcaY3b4\nX3/LGBNe5/wgw41PJbFy7PO8OXgSnUYOILJzu1JtfN8fY91jfyJt+ad1jcEEGYbOSuJvSc+zcOgk\nut4xgNZlcnqMTuDUiXwWxE9g68JVJDwxRjkuywHoMLgXnjgvC+InsHryQoY9dX+F7YbPfoDVkxey\nIH4CnjgvcQk9A6ovylFOsYae0072RzmaB27bN07OgTsThzF/zlOVLl+/cQsHDqWzculCZkz6NbP+\nkFynHCfmmlM5bpsHTs636xJ60zbOy38m/IbXpyxgzOyHKmz3xQep/H7k1DplgHPz7XJS6II/gcrp\nW9NfB8oegWOA54D7rLXdgFuBecaYSP/y8dbaXtbansAB4NG6hkf37kju/ix8B45QePY8aX/fRPvh\nfUu1yTt0lOw9B7GFdX8eIrZ3R3L2Z3HiYFHOnhWb6DSsdE7nYdeza9l6AL5euZmrb+ymHJflAHQa\n1pfdyzYAkLH9W0JbhhEWHVmqTVh0JCHhzUlPTQNg97INdB7eL6D6ohzlFGvoOe1kf5SjeeC2fePk\nHOjXuwetWkZUuvzDDZu449YhGGPo1b0rPl8eR45m1zrHibnmVI7b5oGT863n8B+z+e2PAdi//Rua\nR4TRsm1kuXb7t39D7pGcOmWAc/NNBJwvxN8CbjPGNAMwxrQH2gEfW2u/AbDWpgOHgbb+n3P9bQ3Q\nHOr+iQEtYj3kZVz8SyA/M5uwWE9dV1epcK8HX4kcX0Y2EV5PuTa56UVt7PlCTvtO0txTu4v9ygns\nHIAIr4fc9GMXszKziYgpnRUR48GXWfX2NHZflKOcYg09p4u31U3j5rYccM88cNu+cXIOVCfryDG8\n0W0u/BwT3YasI7W/JdmJueZUjtvmgZPzLTLGw/ES+ycn8xiR3qhar6c6Ts03EXC4ELfWHgM2U3TV\nG4quhi+11l4oro0x/YEQ4NsSr/0ZyASuBV6sa77BVLBRdV1b7XJsmZyi3ytU3UY5l3eOf0UVrKdc\nWPVtKlu9y8ZMOYGd419RBeupvzkN7hs3t+X4V1TBei6/eeC2fePoHKhGRfu6ouxqOTDXnMpx2zwI\nuHNOfXBqvonQOB/WVnx7+t/9/32weIExJhZYDCRZay/c0m+tfcAYE0xRET4a+HPZlRpjHgYeBvhF\nZH8GhXUuF5yfkU147MXfnoV5o8jPPF4/vSrBl5lNRImciNgo8rJK5/gysmnZLoq8zGxMcBDNIlpw\nKidPOS7I6XPfUHqOGQxA5s69tGzXmu+Ls7xR5B0ufcuULzObCG/Z7anZbVVuGTPlBHaOk3Paif4o\nR/PAyQw35tSEN7oNmYcvXgHPOnyU6Data/Rep+aaG+e0m3JuGjucG+4ZAsB3O77F0+7i/In0tuZE\nVv38G97peXC5KdQvGRpMY3x92XJgiDHmeqC5tTYVwBjTEngXeNJau6nsm6y154GlwKiKVmqtfcVa\n289a26+iIhzg8I69tIrzEnFVW4KaBtNp5AC+W5NaT926KGPHXjxxXlr5c7rePoC0Mjlpa1PpPmoQ\nANck9ufAp18qxyU5219by6LEqSxKnMo372+j26iBAMT26chp30nyy5zQ8w/ncCb/FLF9OgLQbdRA\n0tZsC4i+KEc54OycdqI/ytE8cDLDjTk1kTBwACmrPsBay45dewgPD6Ntm5rdSuzUXHPjnHZTzseL\n3+fZxN/xbOLv2Pn+FvrffRMA7ft0psB38pKeBS/J6XkgUsw0xq0Uxpg3gS7AcmvtDGNMCPAesMJa\nO69EOwN0tNam+f//9wDW2olVrf9PP7y30k5ddXMvbphxLyYoiK+XfsT2F1PoN3EUR3bs47s1qbTt\n1YHhr/6WZq1acP70WU4ePsHfhjxR4bpOBFe+DR0G9+LmafdigoP44s2P2JScwsDHRpG5cx9pa1MJ\nbtaUEXPHEdOtPady8kh5NJkTB49U1S3lXIY5AENnJREX35NzBWd4b+IrZH5R9HUbSStnsyix6JM9\nvT3i+OkLD9MkNIR963awdtprAdcX5SinWEPPaSf7oxzNA7ftm/rMGb9tZqU5j09/li3bd5KTk0vr\nqEgeeWgs586dA2D0XSOw1jJ7zkts2LSV5qGhzJoynu5du1S4rrl9p1XZJyfmmlM5l+M8cCrngDlb\nZdbPZz5I1/henC04w5LHX+bAF3sBeGLlczyb+DsARj7xC/qNvJFWMR5OZB1n49J/sHLeWxfWcbVt\nWm2f6mseTPpuSR2exQg8Y39092V/SXzxd28H5L5orEL8LuBtoKu19itjzL0U3W6+u0Sz+4GdwHqg\nJWCAHcC/F3+AW2WqKsTrU1WFuIiIiMjlrKpCvD5VV4jLlaG6Qrw+1KQQry8qxANHoBbijfGMONba\nd+DiJzxYa5cASyppfqMjGyUiIiIiIiIXXPZVeABrjGfERURERERERK5YKsRFREREREREHKRCXERE\nRERERMRBjfKMuIiIiIiIiAS2Qj0l3mB0RVxERERERETEQSrERURERERERBykQlxERERERETEQXpG\nXERERERERMqxeka8weiKuIiIiIiIiIiDVIiLiIiIiIiIOMiVt6ZnBzuT41CMiIiIiOPm9p3mSM74\nbTMdyXGqP1I37QubNnjGOdPgEa5T2Ngb4GK6Ii4iIiIiIiLiIBXiIiIiIiIiIg5SIS4iIiIiIiLi\nIFc+Iy4iIiIiIiKXplBfX9ZgdEVcRERERERExEEqxEVEREREREQcpEJcRERERERExEF6RlxERERE\nRETKsXpGvMHoiriIiIiIiIiIg1SIi4iIiIiIiDhIhbiIiIiIiIiIg67IZ8SHzRhLx8G9OVtwmv+Z\n+ApZu/aXa+Pt3p4RL/ySpqEhfPvh56yZsbhWGXHxPRkyfSwmOIidb6zjs5dXlFoeHNKEEXPGEdMj\njoLjPlIeTSb30NE69WfIjLF08PfnvUr6E9O9PYkv/JImoSHs/fBzPqhlf5zK0bgFdo6b+uJUjlNz\n2m3Hjtv6o5zAnQduO0bdduw8+fQcPv5kM1GeSJYvmV9uubWWZ+bNZ/3GLYSGNmP21Alcd02nWvdF\n+yewzwXt43ty84yicfvijXVsfqn8uP10btG4nTruY8Wv6j5uQ0vUCe9W0Z+SdcLaOozb5aKwsTfA\nxRy9Im6MWWeMuaXMa781xqw0xmw0xuw2xuw0xoyu4L0vGmPyLnUbOg7uhSfOy/z4Cbw3eSG3PnV/\nhe1umf0AqyYvZH78BDxxXjok9KxxhgkyDJ2VxN+Snmfh0El0vWMArTu3K9Wmx+gETp3IZ0H8BLYu\nXEXCE2Pq1J8O/v4siJ/A6skLGVZJf4bPfoDVkxeywN+fuFr0x6kcjVtg57ipL07lODWn3XbsuK0/\nygnceeC2Y9SNx86dicOYP+epSpev37iFA4fSWbl0ITMm/ZpZf0iu1fpB++eyOBc8lcSypOf585BJ\nXFvFuC28aQJbX13FTZMvbdz+FD+BVZMXcks1dcKf6lAniBRz+tb014GyR8YY4DngPmttN+BWYJ4x\nJrK4gTGmHxBJPeg8rC+7lm0AIH37tzRrGUZYdOlVh0VH0iy8Od+npgGwa9kGugzvV+OM2N4dydmf\nxYmDRyg8e549KzbRaVjfMttxPbuWrQfg65WbufrGbnXqT6dhfdnt70/G9m8JraQ/IeHNSff3Z/ey\nDXSuRX+cytG4/X/27jy8qure//h7JciYBJJAchInwigyD0VuRUmYlCA40F/VWyAOvYpWbRUcAK9Q\ngdbrVaAtdUCpRWirrSMoIkNBpRURwuzEeAEzMISQEwiTWb8/zgnJOTlJzgnJJhw+r+fhIclee333\nd+21187K3vvsuh0nnHJxKo5TfTrcjp1wy0dx6m4/CLdjNByPnV7dOtM0JrrC5StWrWb49QMwxtC1\nUwfc7kIOHMwLKYb2T90eC1zdWnN4dy5H9nja7ZuFq2k92LfdWg/uwda3PO323Vm0WyjzhKwy84RQ\n200EnJ+IvwXcYIxpAGCMaQkkA59aa7cBWGuzgP1AC2+ZSOB/gcdqYgOiXbEUZB068707J4/oxFjf\nMomxFOSUDuIF2XlEu3zLVCbKFYs7u3R9d4D1o1yxFGR5ytgfijnhPkaj2KiQcoHg83HnVL49dSGO\n2q1uxwmnXJyK41SfDrdjJ9zyUZzqxXGiH4TbMRqOx05Vcg8cwpXQ/Mz3iQnNyT0Q2i3J2j8Vb09d\niBPtisWdVbp+YXaAGGXK2B+KOXkW7eauA/1aLgyOPiNurT1kjFmD56r3+3iuhr9prT3zgjpjTG+g\nPrDD+6MHgAXW2mxjzNlvRKA6rPUrUr6MtcG/Q88QaH3/zai6THDBgtjWs8zHqThqtzoeJ5xycSiO\nU3063I6dcMtHcaoXx4l+EG7HaFgeO1UIVF+ovy9q/1RSpi7ECaZNznG7VS/Y+aGmj1kpdS4+rK3k\n9vSSifhdJQuMMUnAPCDDWltsjEkG/h+QWlWlxph7gHsAborrTe+otmeW9Rg9kG63pQGQvWknMcnx\nZ5ZFu+Jw78/3qasgJ48YV9yZ72OS4ijM9S1TGXdOHtFJpetHJ8VRmHvYt0x2HjHJcRTm5GEiI2gQ\n3Zjj+cE9At999EC6ePPJ8ebzfZl8Cv3ycefkEe3y356q83Eqjs/6arc6FSeccnEyjs/6tdinnYoT\nbu0Wbv0t3OL4rF/Lx0+4HKNOxXG6DwTDldCcnP2lV8Bz9x8koXl8JWuUp/1Tdnvq4FiQnUd0cun6\nUUlxFO4v327RZdqtfgjt1mP0QLqWmSdE+80TgsnHXcP9Wi4M5+L1Ze8BA4wxPYBG1tpMAGNMDPAh\n8KS1drW3bHegDbDdGLMbaGyM2R6oUmvtbGttL2ttr7KTcIDM15fxp/SJ/Cl9It8tWUenEX0BSO7e\nmhPuYxz1O8CO7s/n5NHjJHdvDUCnEX3ZtnRd0Almb9xJbIqLppe2IOKiSDoM68P2pZk+ZbYvy6TT\niGsAaJ/emz3//iro+te/voy56ROZmz6RbUvW0dGbT1IV+SR58+k4oi/bg8jHqTgl1G51L0445eJk\nnBK13aedihNu7RZu/S3c4pRw4vgJl2PUqThO94FgpPbtw4LFy7HWsnHL10RFNaFF87iqVyxD+6du\njwU5fu12xbA+7PBrtx1LM+n4E0+7tUvvzd4Q2i3z9WW8lj6R17z51PY8QaSEORe3Gxhj/g60A96z\n1k42xtQHPgIWWmtnVrJeobW2ygc+fnv5yEqTGjwlg1b9unCq6CQfjptNzuZdANy1aBp/Sp8IgKtz\nCjc8f4/nNQsrN7LkqdfL1RNZSYxWaV3p/9RIz2sW/v4Jq2ctoO8jI8jZtIvtyzKJbHARQ2eMIbFj\nS47nF7LggVkc2XugqtQCGjglg5R+XThddJKPyuSTsWgac8vkM8Sbz66VG1kWIJ+6EEftVrfjhFMu\nTsVxqk+H27ETbvkoTt3tB+F2jJ6Px87D656uMMajk57hy/WbyM8vID6uGfffPYrTp08DcOvNQ7HW\nMm36C6xavZZGDRsyZcLDdOrQLmBdM3o+VWEc7Z9zPxZEVPLbe0paV9ImjSQiMoLNb37CF7MWcPUj\nI8jZvIsdSz3tlj5zDAnedvvggVkc2VO+3U4H8dTCoDLzhEVl8rlz0TReK5PP0DLzhKUB8nni/+bX\nwDO1597Nlw077+9Nf3fPwjq5L87VRPxm4B2gg7X2G2PMSOA1YGuZYndYazf4rVcjE/GaUtlEXERE\nRESqVtlEvCZVNhGXc6+yiXhNCWYiXlPCZSJ+42U3nPcT8ff3fFAn98W5eEYca+27UPqJFdba+cD8\nINYL/eMPRUREREREROqQc/GMuIiIiIiIiMgF65xcERcREREREZG6rfhcb0AY0xVxEREREREREQdp\nIi4iIiIiIiLiIE3ERURERERERBykZ8RFRERERESkHMt5//ayOktXxEVEREREREQcpIm4iIiIiIiI\niIM0ERcRERERERFxkJ4RFxERERERkXKK9Yx4rQnLifhnNs+ROKkmzpE4IiIiIuFqRs+nHInz8Lqn\nHYnjVD7hJj+iuNZjNCvWzcBSd6g3ioiIiIiIiDhIE3ERERERERERB4XlrekiIiIiIiJydqzVM+K1\nRVfERURERERERBykibiIiIiIiIiIgzQRFxEREREREXGQnhEXERERERGRcmr/pXIXLl0RFxERERER\nEXGQJuIiIiIiIiIiDtJEXERERERERMRBF+Qz4vf++l5+lPYjThSdYPrY6ezYssNneYOGDRj/4niS\nLk+iuLiYL5Z9wZ+f+XPIcQZMHkWrtG6cKjrBR+Nmk7tld7kyiZ1akv78vdRrWJ+dKzawfPK8Ohkn\npV8XBkwahYmMYNMbK/nixYU+yyPr12Po9DEkdk6h6LCbBQ/MomDfwZBzUZzQ4ziVC4RXX3MqH6fi\nhFOfLhFO7aZ86nY+4TQWhFscJ/rAk7+Zzqf/WkNcbDPem/9SueXWWn478yU++/xLGjZswLSJY7my\nfZuQYjiZT4lw2T8lhk4aTbu0bpwqOsnb414ie2v5fJI7pXDLc/dyUcP6fLdiAx/++vWQYrTs14X+\nkz35bH5jJWteKJ/PkBmefI4fdrPwF9XP53xh0XvEa4ujV8SNMSuNMdf5/exXxphFxpjPjTFbjTGb\njDG3lln+Z2PMLmPMBu+/bmezDb3SenFxy4v5+bU/5/dP/J4Hpj0QsNw7s9/h3v738uCQB7my15X0\nSu0VUpxWaV2JTXHxSr+xfDx+DoOm3hGw3OBpd/Lx+Dm80m8ssSkuUlK71Lk4JsIwcEoG/8h4ljkD\nH6PD8D7Et032KdP51lSOHznKK/3GsnbOYlKfuC2kPBSnenGcygXCq685lY9TccKpT5cIp3ZTPnU7\nn3AaC8ItjlN94Kb0Qbw0fWqFyz/7/Ev27Mti0ZtzmPzYQ0x5blbIMUDH6Nnk0y61G/EpLmakPsJ7\nE15l+LS7ApYbPvUu3p8whxmpjxCf4qJtatfQ8pmawdsZz/LagMe4opJ85lw7lrWvLuba8dXLRwSc\nvzX9b4B/j70N+B9gtLW2I3A9MNMY06xMmUettd28/zaczQb0GdyH5W8vB+Db9d/SJKYJsQmxPmVO\nHD/Bps83AXD61Gl2bNlBfFJ8SHHaDOrJ1rdXAZC9fgcNY5rQJKGZT5kmCc2oH9WIrMztAGx9exVt\nB4c24XciTlK31uTvzuXI3gMUn/qBrxeups2gnj5l2g7qwZa3PwPg20VruOzqjiHloTjVi+NULhBe\nfc2pfJyKE059ukQ4tZvyqdv5hNNYEG5xnOoDvbp1pmlMdIXLV6xazfDrB2CMoWunDrjdhRw4mBdy\nHB2j1c+nw+CebHjHU8++9dtpGN2YqBa++US1aEaD6EbszdwGwIZ3PuPKEPJxdWvN4d25HNnjyeeb\nhatpPdg3n9aDe7D1Lc92fHcW+YiA8xPxt4AbjDENAIwxLYFk4FNr7TYAa20WsB9oURsb0NzVnAPZ\nB858fzDnIM1dzSss3ySmCb0H9mbjvzaGFCfaFUtB1qEz37tz8ohO9J3wRyfG4s4pHcjd2XlEu3zL\n1IU4Ua5Y3NmVrx/liqUgy1PG/lDMCfcxGsVGhZSL4oQex6lcILz6GugYrat9ukQ4tRson4q2pzJO\n5RNOY0G4xXGyT1cm98AhXAmlvysmJjQn90DotyPrGD2LfBJjOZJVGqsgJ48Yv1gxrlgKymzPkezy\nOVcawxWLu0yMwgDrly1jfyjmZC30t7qmGHve/6urHJ2IW2sPAWvwXPUGz9XwN621Z1rIGNMbqA+U\nfXB7mveW9Rklk/ga3q6AP4+IjODxPzzOgtcWkLMnJ7RKjak6TjBl6kAcQ6D1/UNUXUZxaj6OU7l4\nKwpQz/nZ17wVBahHx2hdiOOtKEA952e7eSsKUI/yqTSEU/mE0VgQbnEc7dOVCLTNgeJWRcdo4DJB\nxQrU3kHECmkOFsy2OtDf5MJxLj6sreT29Pe9/595yMMYkwTMAzKstSXvjx8P5OCZnM8GHgee9q/U\nGHMPcA9Ax9iOXBZ12ZllN4y+getu9zyavm3TNloklV5sb+5qzqHcQwTy0DMP8f3u73l/zvtBJdZ9\n9EC63JYGQM6mncQkx/O9d1m0K47C/fk+5d05eUS74s58H50UR2Gub5lzGcdn/ST/9Q/7lsnOIyY5\njsKcPExkBA2iG3M8vzDoGIpTvTi1HSPc+pqO0brdp8Ot3ZRP3c0n3MaCcIvjs74DY1tVXAnNydlf\negU8d/9BEpqH9sgi6BgNNZ+rRg2i1+2efL7fuJOmyaWxYlxxFPjFOpKdR0yZ7WmaFId7v2+ZSvPJ\nziO6TIyopDgK95fPJ7pMPvVrob/JheNcvL7sPWCAMaYH0MhamwlgjIkBPgSetNauLilsrc22HieA\n14DegSq11s621vay1vYqOwkH+OD1D3hwyIM8OORBPv/4cwaMGABA++7tOeo+yuEAB+nocaNpEt2E\n2ZNnB53Y+teXMTd9InPTJ7JtyTo6jugLQFL31pxwH+Oo3wB4dH8+J48eJ6l7awA6jujL9qXr6kyc\nEtkbdxKb4qLppS2IuCiSDsP6sH1ppk+Z7csy6TTiGgDap/dmz7+/Crp+xal+nNqOEW59Tcdo3e7T\n4dZuyqfu5hNuY0G4xSnh1NhWldS+fViweDnWWjZu+ZqoqCa0aB5X9Yp+dIyGls8X85byx/QJ/DF9\nAl8tWUu3Wzz1XNK9DSfcRRQe8M2n8EA+JwqLuKS75xPtu91yDV8vCT6fHL98rhjWhx1++exYmknH\nn3i2o116b/bWQn+TC4cJ+VakmghqzN+BdsB71trJxpj6wEfAQmvtTL+ySdbabOO532QGcNxa+0Rl\n9adfll5pUvdPuZ+eqT05UXSCGeNmsG2T50Md/vDRH3hwyIPEu+KZt2Yee7bt4dTJUwB8MPcDPn7j\nY596Uk3lg/DAKRmk9OvC6aKTfDRuNjmbdwGQsWgac9MnAuDqnMKQ5++hXsP67Fq5kWVPhfaaBafi\ntErrSv+nRnpe5/D3T1g9awF9HxlBzqZdbF+WSWSDixg6YwyJHVtyPL+QBQ/M4sjeA1VXrDhnHcep\nXCC8+ppT+TgVJ5z6dIlwajflU7fzCaexINzi1GQfeHhduRsqAXh00jN8uX4T+fkFxMc14/67R3H6\n9GkAbr15KNZapk1/gVWr19KoYUOmTHiYTh3aVbjNM3o+5Ug+VTnf9k+BKQ748xI3PH0H7fp15WTR\nCd559GWyvPn8YtFv+GP6BACSO6cw4rkxnteXrdzIB5P+7FNHs+LKr0GmpHUlbdJIIiIj2PzmJ3wx\nawFXPzKCnM272LHUk0/6zDEkePP54IFZHNkTOJ9xe+aH/vxCHTTgksHn/c33y/ctqZP74lxNxG8G\n3gE6WGu/McaMxHO1e2uZYndYazcYY/6J54PbDLABGGOtrfQekKom4jWlqom4iIiIiNQNFU3Ea1pl\nE3GpWFUT8ZpQ1US8JmkiXnfU1Yn4uXhGHGvtu1D6CQ/W2vnA/ArK9ndqu0RERERERERq27l4RlxE\nRERERETkgnVOroiLiIiIiIhI3VaX38N9vtMVcREREREREREHaSIuIiIiIiIi4iBNxEVEREREREQc\npGfERUREREREpByrZ8Rrja6Ii4iIiIiIiDhIE3ERERERERERB2kiLiIiIiIiIuIgPSMuIiIiIiIi\n5RRbPSNeW8JyIt4rItaZQOqXIiIiIueFGT2fciTOw+uediSOU/k4JcbqRl25sKjHi4iIiIiIiDgo\nLK+Ii4iIiIiIyNnRDcC1R1fERURERERERBykibiIiIiIiIiIgzQRFxEREREREXGQnhEXERERERGR\ncor1lHit0RVxERERERERET/GmDhjzFJjzDbv/+Xek22MudwYs84Ys8EYs9UYMyaYujURFxERERER\nESnvCWC5tbYtsNz7vb9s4MfW2m7AVcATxpjkqirWRFxERERERESkvBuBud6v5wI3+Rew1p601p7w\nftuAIOfYekZcREREREREytEz4iRaa7MBrLXZxpiEQIWMMZcCHwJtgEettVlVVayJuIiIiIiIiIQl\nY8w9wD1lfjTbWju7zPJlgCvAqhODjWGt3Qt08d6S/p4x5i1rbW5l61yQE/H0SaNpm9aVU0UneXfc\ny2Rv3V2uTFKnltzy3BjqNbyIbSs2sujXr4ccZ8DkUbRK68apohN8NG42uVvKx0ns1JL05++lXsP6\n7FyxgeWT54UUI6VfFwZMGoWJjGDTGyv54sWFPssj69dj6PQxJHZOoeiwmwUPzKJg38GQc3EqDoRX\nuzmVj1NxwikXxVEcCL+xINzOCU7FCac+7VSccOsDTsR58jfT+fRfa4iLC/gRKwAAIABJREFUbcZ7\n818qt9xay29nvsRnn39Jw4YNmDZxLFe2bxNyLiXCqb85Fadlvy70n+zpB5vfWMmaF8r3gyEzPP3g\n+GE3C39R/XOCOMc76Z5dyfKBFS0zxuQaY5K8V8OTgP1VxMoyxmwFrgHeqqyso8+IG2NWGmOu8/vZ\nr4wxi4wxn3s/ZW6TMebWMsuNMWaaMeY7Y8zXxpiHzmYb2qZ2JT7Fxe9Sx7JgwhyGTbszYLlhU+9i\nwYRX+V3qWOJTXLRN7RpSnFZpXYlNcfFKv7F8PH4Og6beEbDc4Gl38vH4ObzSbyyxKS5SUrsEHcNE\nGAZOyeAfGc8yZ+BjdBjeh/i2vp8L0PnWVI4fOcor/cayds5iUp+4LaQ8nIwD4dVuTuXjVJxwykVx\nFAfCbywIt3OCU3HCqU87FSfc+oBTcW5KH8RL06dWuPyzz79kz74sFr05h8mPPcSU52aFHKNEOPU3\np+KYCMPAqRm8nfEsrw14jCsq6Qdzrh3L2lcXc+346p0T5LyyAMjwfp0BvO9fwBhziTGmkffrWOBq\n4NuqKnb6w9r+Bvj32NuA/wFGW2s7AtcDM40xzbzL7wAuBa6w1nYA3jibDbhicE82vPMZAPvWb6dh\ndGOiWjTzKRPVohkNohuxN3M7ABve+YwrBvcMKU6bQT3Z+vYqALLX76BhTBOaJPjGaZLQjPpRjcjy\nxtn69iraDu4VdIykbq3J353Lkb0HKD71A18vXE2bQb7b2XZQD7a87cn320VruOzqjiHl4WQcCK92\ncyofp+KEUy6KozgQfmNBuJ0TnIoTTn3aqTjh1gecitOrW2eaxkRXuHzFqtUMv34Axhi6duqA213I\ngYN5IceB8OpvTsVxdWvN4d25HNnj6QffLFxNa7/f/1sP7sHWtzz94LuzOCecT6y15/2/s/QMMMgY\nsw0Y5P0eY0wvY8yr3jIdgC+MMRuBT4DnrLWbq6rY6Yn4W8ANxpgGAMaYlkAy8Km1dht4LufjueTf\nwrvOfcDT1tpi7/JKbweoSkxiHEeyDp35viAnjxiX7+vgYlyxFGSXDnwF2XnEJMaFFCfaFUtBmTju\nnDyiE33jRCfG4s4pjePOziPaVe7VdBWKcsXizq58/ShXLAVZnjL2h2JOuI/RKDYqpFycigPh1W7g\nTD5OxQmnXBRHcSD8xoJwOyc4FSec+rRTccKtDzg5FlQm98AhXAnNz3yfmNCc3APVu+05nPqbU3Gi\nXbG4s0rXL8wOEKNMGftDMSdroR9I3WKtPWStHWCtbev9P8/787XW2p97v15qre1ire3q/b/C2+DL\ncnQibq09BKzBc9UbPFfD37Rl/lRhjOkN1Ad2eH/UGrjVGLPWGPORMabt2WyDMQG3K5hCZx0omDih\n/NXGEGh9/xBVl6krcbwVBajn/Gw3b0UB6qnZfByLE065KI7iEH5jQbidExzbP2HUp52KE259wNGx\noBKB9kGguEEJo/7mWJxg9rED/UAuHOfiw9pKbk9/3/v/XSULvA/AzwMySq6A43kX23FrbS9jzC3A\nn/A8/O6j7KfhDY3rTY/o0g+36D1qED1vTwPg+407aZocf2ZZjCsOd26+T10F2XnEJJVeAY9JiqNg\n/+EqE+s+eiBdbvPEydm0k5jkeL73Lot2xVG43zeOOyePaFdpnOikOAr9tqUy7pw8opP81/fdTnd2\nHjHJcRTm5GEiI2gQ3Zjj+YVBx3AiTri1m1P5OBEnnHJRHMXxFy5jgVP5hFOccOvT6mvnR5yquBKa\nk7O/9Ap47v6DJDSPr2QNX+HW3xzv19l5RCeXrh+VFEfh/vL9ILpMP6hfC/1ALhxO35oO8B4wwBjT\nA2hkrc0EMMbE4Hn32pPW2tVlyu8D3vZ+/S4Q8FMXrLWzrbW9rLW9yk7CAdbMW8qL6RN4MX0C3yxZ\nS7dbPPP4S7q34bi7iMIDvgdp4YF8ThYWcUl3Tz3dbrmGb5asqzKx9a8vY276ROamT2TbknV0HNEX\ngKTurTnhPsZRvwHj6P58Th49TlL31gB0HNGX7UurjlMie+NOYlNcNL20BREXRdJhWB+2L830KbN9\nWSadRnjybZ/emz3//iro+p2KE27t5lQ+TsQJp1wUR3H8hctY4FQ+4RQn3Pq0+tr5EacqqX37sGDx\ncqy1bNzyNVFRTWjRPPhHI8Otvzndr3P8+sEVw/qww68f7FiaScefePpBu/Te7K2FflDXFGPP+391\nlamBB9hDD2rM34F2wHvW2snGmPrAR8BCa+1Mv7LPAN9Za/9kjEkF/tda+6PK6n+q5c8qTWro03fQ\ntl8Xz+vLHn2ZrM27ALhv0W94MX0CAMmdU7j5uXu5qGF9tq3cyIeT5parJ8pWfrvQwCkZpPTrwumi\nk3w0bjY53jgZi6YxN93zWjpX5xSGPH8P9RrWZ9fKjSx7KrTXpLVK60r/p0Z6XrPw909YPWsBfR8Z\nQc6mXWxflklkg4sYOmMMiR1bcjy/kAUPzOLI3gMhxXAyDoRXuzmVj1NxwikXxVEcCL+xINzOCU7F\nCac+7VSccOsDNRnn4XVPB/z5o5Oe4cv1m8jPLyA+rhn33z2K06dPA3DrzUOx1jJt+gusWr2WRg0b\nMmXCw3Tq0K7CbZ7R86lKcwqn/lZTcSKqmPakpHUlbdJIIiIj2PzmJ3wxawFXPzKCnM272LHU0w/S\nZ44hwdsPPnhgFkf2BO4H4/bMr+ZzBXVL7+R+dXcmG6Q1WZ/UyX1xribiNwPvAB2std8YY0YCrwFb\nyxS7w1q7wfvp6X8BLgMKgTHW2o2V1V/VRLymVDURFxEREZELS0UT8ZpW1URcyqtqIl6TNBGvO+rq\nRPxcPCOOtfZdKP1kDGvtfGB+BWXzgaEObZqIiIiIiIhIrTonE3ERERERERGp22wdfsb6fHcuPqxN\nRERERERE5IKlibiIiIiIiIiIg3RruoiIiIiIiJRzLj7Y+0KhK+IiIiIiIiIiDtJEXERERERERMRB\nmoiLiIiIiIiIOEjPiIuIiIiIiEg5xXp9Wa3RFXERERERERERB2kiLiIiIiIiIuKgsLw1vbE153oT\nREREROQCNKPnU47EeXjd047EcSqfRsW1H+O0pghSh4TlRFxERERERETOjt4jXnt0a7qIiIiIiIiI\ngzQRFxEREREREXGQJuIiIiIiIiIiDtIz4iIiIiIiIlKO3iNee3RFXERERERERMRBmoiLiIiIiIiI\nOEgTcREREREREREH6RlxERERERERKcfqGfFaoyviIiIiIiIiIg66IK+ID5w8itZp3ThVdIIPx80m\nd8vucmUSO7Vk6PP3clHD+uxYsYFlk+eFFCOlXxcGTBqFiYxg0xsr+eLFhT7LI+vXY+j0MSR2TqHo\nsJsFD8yiYN/BauUzYPIoWnnz+aiSfNKfv5d6Deuzc8UGliufsMrHiThqM8VxMk649Ten8gm3OODM\n/gmnY8epOOHWB8IpzpO/mc6n/1pDXGwz3pv/Urnl1lp+O/MlPvv8Sxo2bMC0iWO5sn2bkPNwKh+A\ny1K7cO1kT3/76m8rWfeCb3+LqF+PwTPH0KJzCscPu1l8/yzc1ehvl/frQurkUURERrDljZV8+UL5\nfn3djNJ+vegX1e/XIo5eETfGrDTGXOf3s18ZYxYZYz43xmw1xmwyxtxaZvlnxpgN3n9Zxpj3zmYb\nWqV1JTbFxcv9xrJ4/Byum3pHwHLXTbuTxePn8HK/scSmuGiV2iXoGCbCMHBKBv/IeJY5Ax+jw/A+\nxLdN9inT+dZUjh85yiv9xrJ2zmJSn7jtrPJ5pd9YPh4/h0EV5DN42p18PH4Or3jzSVE+YZOPE3HU\nZorjZJxw629O5RNuccCZ/RNOx45TccKtD4RbnJvSB/HS9KkVLv/s8y/Zsy+LRW/OYfJjDzHluVkh\n5VCWU/0tdWoGC0Y/y1/6P0a7G/sQ69ffOt6WyvH8o8y7ZiwbXl3M1ROqN7b1n5rBexnPMnfAY7Qf\n3oc4/zi3pnLiyFFeu3Ysma8upu/46vVrEXD+1vS/Af499jbgf4DR1tqOwPXATGNMMwBr7TXW2m7W\n2m7A58A7Z7MBbQf1ZMvbqwDIWr+DBjFNaJLQzKdMk4RmNIhqRFbmdgC2vL2KtoN7BR0jqVtr8nfn\ncmTvAYpP/cDXC1fTZlBPv+3owZa3PwPg20VruOzqjtXKp82gnmz15pO9fgcNK8infpl8tiqfsMrH\niThqM8VxMk649Ten8gm3OODM/gmnY8epOOHWB8ItTq9unWkaE13h8hWrVjP8+gEYY+jaqQNudyEH\nDuaFlEcJJ/JJ9Pa3gj2e/vbdgtW0Guzb31IG9+Cbtzz9bfuHa7ikGv3NVdKvvXG+Xbia1n5xWg/u\nwVfeONvOol+fT4qtPe//1VVOT8TfAm4wxjQAMMa0BJKBT6212wCstVnAfqBF2RWNMdFAf+CsrohH\nu2JxZx068707J4/oxFjfMomxuHNKByR3dh7RLt8ylYlyxeLOrnz9KFcsBVmeMvaHYk64j9EoNiqk\nXMCTT4HyuaDzcSKO2kxxnIwTbv3NqXzCLQ44s3/C6dhxKk649YFwjFOZ3AOHcCU0P/N9YkJzcg9U\n7/ZqJ/Jp4oqlMKt0/cLsPKIC9Dd3mf520n2MhtUZ2/zjJFYe50Q14oiUcHQibq09BKzBc9UbPFfD\n37S29E8VxpjeQH1gh9/qNwPLrbUFZ7URxgTarirLEMJfUwyBYviHqLpMcMGql0+5MpWFUD4BywQX\nrPbzcSKO2kxxnIwTbv3NqXzCLY63ogD11HC/DqNjx6k4YdcHwjFOJQLVFWh/BcWJ/hZUXwr0u3vQ\nIUoCVRknYDvV3QuuUsediw9rK7k9/X3v/3eVLDDGJAHzgAxrbbHfercDr1ZUqTHmHuAegJvjetM7\nqu2ZZT1GD6TrbWkAZG/aSXRy/Jll0a44Cvfn+9Tlzskj2hVXWiYpDneub5nKuHPyiE7yXb8w97Bv\nmew8YpLjKMzJw0RG0CC6McfzC4Oqv/vogXTx5pOzaScxyfF8H2I+hcrnvM/HyXZTmymOjtG6m0+4\nxXFi/4TbsRNufTrc2s3p/VMVV0JzcvaXXgHP3X+QhObxlazhy+l8CrPziEouXT8qKY6jfv2tMCeP\n6OQ4jnr7W/1qjG2F2Z46fOLsL9+vo89yDD3f6PVltedcvL7sPWCAMaYH0MhamwlgjIkBPgSetNau\nLruCMSYe6O1dHpC1dra1tpe1tlfZSThA5uvLeC19Iq+lT2TbknV0GtEXgOTurTnhPsZRvwHj6P58\nTh49TnL31gB0GtGXbUvXBZ1g9sadxKa4aHppCyIuiqTDsD5sX5rpU2b7skw6jbgGgPbpvdnz76+C\nrn/968uYmz6Rud58OnrzSaoinyRvPh1H9GW78jnv83Gy3dRmiqNjtO7mE25xnNg/4XbshFufDrd2\nc3r/VCW1bx8WLF6OtZaNW74mKqoJLZrHVb3iOcond+NOmrV0EePtb+2G92GXX3/btTSTK37i6W9t\nhvZm379CH9tyvP26JE77YX3Y6Rdn59JMrvTGaZvem73VGENFSpiavNUl6KDG/B1oB7xnrZ1sjKkP\nfAQstNbODFB+DPAf1tqMYOp/5vKRlSY1aEoGrfp14VTRSRaNm03O5l0A3LloGq+lTwTA1TmFoc/f\n43nNwsqNLH3q9XL1VPZXjFZpXen/1EhMZASb//4Jq2ctoO8jI8jZtIvtyzKJbHARQ2eMIbFjS47n\nF7LggVkc2XsgmPTKGTglg5R+XThddJKPyuSTsWgac8vkM8Sbz66VG1kWIJ/KKJ+6nY8TcdRmiuNk\nnHDrb07lE25xwJn9E07HjlNxwq0PnI9xHl73dMD6H530DF+u30R+fgHxcc24/+5RnD59GoBbbx6K\ntZZp019g1eq1NGrYkCkTHqZTh3YVbu+Mnk85kk8j/3thy7g8rSvXTB5JRGQEX735CWv/sICrxo5g\n/6Zd7Frq6W+DZo6hRaeWnMgvZPEvZlGwp3x/O13FHfgt07qSOsnTr7e++QlrZi3gPx4ZQe7mXez0\nxrl+5hgSvP160QOzOBIgDsDDe+ZX837/uqVj4lXn/SXxrblf1Ml9ca4m4jfj+fTzDtbab4wxI4HX\ngK1lit1hrd3gLb8SeMZauziY+quaiNeUc3E7gYiIiIhIRRPxmlbVRLymVDYRrylVTcRrkibidUdd\nnYifi2fEsda+S5lPVbDWzgfmV1I+1YHNEhEREREREa+6/Pqv850u6oqIiIiIiIg4SBNxERERERER\nEQdpIi4iIiIiIiLioHPyjLiIiIiIiIjUbXqPeO3RFXERERERERERB2kiLiIiIiIiIuIgTcRFRERE\nREREHKRnxEVERERERKQcvUe89uiKuIiIiIiIiIiDNBEXERERERERcZBuTRcREREROc/M6PmUI3Ee\nXve0I3F+38OZfETqCk3ERUREREREpBy9R7z26NZ0EREREREREQdpIi4iIiIiIiLiIN2aLiIiIiIi\nIuXo9WW1R1fERURERERERBykibiIiIiIiIiIgzQRFxEREREREXGQnhEXERERERGRcvT6stqjK+Ii\nIiIiIiIiDtJEXERERERERMRBmoiLiIiIiIiIOOiCfEZ84ORRtE7rxqmiE3w4bja5W3aXK5PYqSVD\nn7+XixrWZ8eKDSybPC+kGCn9ujBg0ihMZASb3ljJFy8u9FkeWb8eQ6ePIbFzCkWH3Sx4YBYF+w5W\nK58Bk0fRypvPR5Xkk/78vdRrWJ+dKzaw/ALPx6k44dZuajP1tXCLA+G1f5SPxjblo3ycivPkb6bz\n6b/WEBfbjPfmv1RuubWW3858ic8+/5KGDRswbeJYrmzfJuQ8AFr260LaZM/+2fLGSta8UH7/DJkx\nhoTOKRw/7OaDX1Rv/zgV53xibfG53oSw5egVcWPMSmPMdX4/+5UxZpEx5nNjzFZjzCZjzK1llg8w\nxmQaYzYYY1YZY6p3BHu1SutKbIqLl/uNZfH4OVw39Y6A5a6bdieLx8/h5X5jiU1x0Sq1S9AxTIRh\n4JQM/pHxLHMGPkaH4X2Ib5vsU6bzrakcP3KUV/qNZe2cxaQ+cdtZ5fNKv7F8PH4OgyrIZ/C0O/l4\n/Bxe8eaTcgHn41SccGs3tZn6WrjFgfDaP8pHY5vyUT5OxrkpfRAvTZ9a4fLPPv+SPfuyWPTmHCY/\n9hBTnpsVUg4lTIRhwNQM3sl4lj8PeIz2w/sQ57d/Onn3z5+uHcu6Vxdz7fjqnXuciCNSwulb0/8G\n+PfY24D/AUZbazsC1wMzjTHNvMtfBH5mre0G/BV48mw2oO2gnmx5exUAWet30CCmCU0SmvmUaZLQ\njAZRjcjK3A7AlrdX0XZwr6BjJHVrTf7uXI7sPUDxqR/4euFq2gzq6bcdPdjy9mcAfLtoDZdd3bFa\n+bQZ1JOt3nyy1++gYQX51C+Tz9YLPB+n4oRbu6nN1NfCLQ6E1/5RPhrblI/ycTJOr26daRoTXeHy\nFatWM/z6ARhj6NqpA253IQcO5oWUB4CrZP/s8eyfbxeups1g3/3TZnAPtr7l2T/fVXP/OBVHpITT\nE/G3gBuMMQ0AjDEtgWTgU2vtNgBrbRawH2jhXccCMd6vmwJZZ7MB0a5Y3FmHznzvzskjOjHWt0xi\nLO6c0oHCnZ1HtMu3TGWiXLG4sytfP8oVS0GWp4z9oZgT7mM0io0KKRfw5FOgfELKx6k44dZuajP1\ntXCLA+G1f0D5VLQ95zpGuLWZ8qnb+TgZpzK5Bw7hSmh+5vvEhObkHgj9Nu4oVyzuLN/tjEosv3/c\nNXHucSCOSAlHnxG31h4yxqzBc9X7fTxXw9+01p55QZ0xpjdQH9jh/dHPgUXGmCKgAOhzVhthTKDt\nqrIM/mUqC0GgGP4hqi4TXLDq5VOuTGUhwiwfp+KEXbupzQKXqQNxnGq3cIvjrShAPefn/vFWFKAe\n5XOuY4RbmymfwGWCCxY+556qBKorUDtWJeA6/lXXxLnHoTjnm2K9R7zWnIsPayu5Pb1kIn5XyQJj\nTBIwD8iwpZ8M8DCQbq39whjzKDAdz+TchzHmHuAegJvjetM7qu2ZZT1GD6TrbWkAZG/aSXRy/Jll\n0a44Cvfn+9Tlzskj2hVXWiYpDneub5nKuHPyiE7yXb8w97Bvmew8YpLjKMzJw0RG0CC6McfzC4Oq\nv/vogXTx5pOzaScxyfF8H2I+hRdgPmq30PNRm6mvhWOccNs/ykdjm/JRPk7GCZYroTk5+0uvgOfu\nP0hC8/hK1gjMnZ1HdLLfdu733T+F3jJnde5xKI5IiXPx+rL3gAHGmB5AI2ttJoAxJgb4EHjSWrva\n+7MWQFdr7Rfedd8EfhyoUmvtbGttL2ttr7KTcIDM15fxWvpEXkufyLYl6+g0oi8Ayd1bc8J9jKN+\nA9PR/fmcPHqc5O6tAeg0oi/blq4LOsHsjTuJTXHR9NIWRFwUSYdhfdi+NNOnzPZlmXQacQ0A7dN7\ns+ffXwVd//rXlzE3fSJzvfl09OaTVEU+Sd58Oo7oy/YLMB+1W+j5qM3U18IxTrjtH+WjsU35KB8n\n4wQrtW8fFixejrWWjVu+JiqqCS2ax1W9op+cjTtpluIixrt/2g/rww6//bNjaSYdf+LZP+2qee5x\nKo5ICVOTt6AEHdSYvwPtgPestZONMfWBj4CF1tqZZcrVA3KAH1trvzPG3I3n6viIyup/5vKRlSY1\naEoGrfp14VTRSRaNm03O5l0A3LloGq+lTwTA1TmFoc/f43mdw8qNLH3q9XL1VPZXjFZpXen/1EhM\nZASb//4Jq2ctoO8jI8jZtIvtyzKJbHARQ2eMIbFjS47nF7LggVkc2Xugss2u0MApGaT068LpopN8\nVCafjEXTmFsmnyHefHat3MiyAPlUJtzycSpOuLWb2kx9LdziQHjtH+WjsU35KJ+ajvPwuqcD1v/o\npGf4cv0m8vMLiI9rxv13j+L06dMA3HrzUKy1TJv+AqtWr6VRw4ZMmfAwnTq0q3B7f9/jqQqXpaR1\nJXXSSCIiI9jy5id8MWsBP35kBLmbd7FjqWf/DJk5hgTv/vnwgVkc2RP6/qnJOGP3zA/9Pvw66PL4\nLuf9ven/d2hTndwX52oifjPwDtDBWvuNMWYk8BqwtUyxO6y1G7xlnwaKgcPAXdbanZXVX9VEvKac\ni9sJREREREScUtFEvKZVNhE/H4XLRPyyuM7n/UR8T97mOrkvzsUz4lhr34XST8aw1s4H5ldS9l2H\nNk1ERERERESkVumiroiIiIiIiIiDNBEXERERERERcdA5uTVdRERERERE6ja9R7z26Iq4iIiIiIiI\niIM0ERcRERERERFxkG5NFxERERERkXLOxauuLxS6Ii4iIiIiIiLiIE3ERURERERERBykibiIiIiI\niIiIg/SMuIiIiIiIiJRTrGfEa40m4mfhU3vYkTiLczY4EmeYq4cjcX56OqbWYzx16ptajwEwt15L\nR+K0ufKgI3Gm7nA5EqeZQ0NPPqcdiXOJvciROMWORIF6Dp1zGzoU51gY3fsV6VCbNXAoTnakM736\nnrj9jsRZnZ3oSJwNDu0gNz84EqdlsTNjaH6EM/0txjoz6DRy6KTw+x5PORLnocynaz3G7xzKRSQY\nYfTriYiIiIiIiEjdp4m4iIiIiIiIiIN0a7qIiIiIiIiUY9Ez4rVFV8RFREREREREHKSJuIiIiIiI\niIiDNBEXERERERERcZCeERcREREREZFyrN4jXmt0RVxERERERETEQZqIi4iIiIiIiDhIE3ERERER\nERERB12Qz4gPnDyK1mndOFV0gg/HzSZ3y+5yZRI7tWTo8/dyUcP67FixgWWT54Uc555f30uvtF6c\nKDrBzLEz2LFlh8/yBg0b8MSL43Fd7qK4uJg1y9Yw95k/hxxnxvSnGXJ9f44VFXH33Q+zfsOWcmWW\nL/0HrqREioqOAzAk/XYOHDgUUpy7f30PPdN6cqLoBH8Y+zt2+uVTv2EDHn3xcVyXJ1FcXMzaZWuY\n98zckGIkpXahx5RRmIgIdvxtJV/PWuizvMVVV9Dj6ZE063AZ/75vFns/XBNS/WX9928epd/Aqyk6\ndpzHH5rMV5u+qbDsS/Omc+nlFzP02luDrr9ZWjdSnr4LIiPY/9flfD/rXZ/lSfcOI/E/B2BPF3Pq\n0BF2PPICJ/YdCDmP+j/qTdQvHoSICI4v+pBjb/w1YLkG1/aj6aSnybvvHk5/923IcQBGTLqDK9O6\nc7LoBH8Z9yL7tu4qV2bouFvpfcu1NG4axaMdM6oVJ33SaNqmdeVU0UneHfcy2Vt3lyuT1Kkltzw3\nhnoNL2Lbio0s+vXrIcdxIp+Ufl0YMGkUJjKCTW+s5IsXfft0ZP16DJ0+hsTOKRQddrPggVkU7DsY\nchxwZmxr2a8L/Sd78tn8xkrWvFA+nyEzPPkcP+xm4S+ql89lqV24xhvnq7+tJNMvTkT9egyaOYYW\n3jgf3z8LdzXbbcDkUbTytttHlbRb+vP3Uq9hfXau2MDyapwTnIjTsl8X0rzttqWS/ZPgbbcP6vj+\nGTppNO3TunGq6CRvj3uJrABjwaBxP6XbLdfQqGkTnu54V8gxGv74R8SNux8iIyh89yMK/vyGz/Ko\nETcQ/dMbofgHio8dJ2/qdE7t2hNyHFdaF7o/7WmznX9dyTf+57c+V9D96ZE07XAZn4+Zxb6zOL8N\n87bbyaKTvFVBuw0e91O6e9ttcjXaDeAnk+6go3cMnVfBGDqszBg6thpjqFNjDnj6W7sy/S3QuSe5\nUwq3POcZQ79bsYEPq3HucWIsuCy1C9eWOUbXBThGB5c5RhdX8xh1Ysx58jfT+fRfa4iLbcZ7818q\nt9xay29nvsRnn39Jw4YNmDZxLFe2bxNyLiX5ONXfzhfFeo94rQnqirgx5mZjjDXGXFHbG1TJNvzK\nGNP4bOtpldaV2BQXL/cby+Lxc7hu6h0By1037U4Wj5/Dy/3GEpttoYI8AAAgAElEQVTiolVql5Di\n9ErrRXLLZO659r+Y9cQfuH/aLwKWe2f2O9zXfwy/HPIQV/bqQM/UniHFGXJ9f9q2SeGKK/ty332P\n88dZv62w7OjRD9DrR4Pp9aPBIU/Ce6T1JLllMvdfey8vPvFH7p12X8By789+lwf738fYIb/kil4d\n6BFCPibC0PM3d7DyZ8+yKPUxLr/xP4hpe7FPmWPfH+SLX73M/73775C231+/gVdzeatLGdj7Jv57\n7FSefnZ8hWUHD03j2NGi0AJERNDqN//FVz+bxoZ+v6L5TX1p1O4SnyJHN+9i0/WPsXHAIxz6YDWX\nPzkq9EQiIoh+6Ffkj3+MvLsyaNB/AJGXX16umGnUiEY3j+DUV1tDj+F1ZWo3WqS4mJL6S96c8Ao/\nnXZ3wHJbl2fy/I0Tqx2nbWpX4lNc/C51LAsmzGHYtDsDlhs29S4WTHiV36WOJT7FRdvUriHFcSIf\nE2EYOCWDf2Q8y5yBj9FheB/i2yb7lOl8ayrHjxzllX5jWTtnMalP3FatWE6MbSbCMHBqBm9nPMtr\nAx7jikrymXPtWNa+uphrx4eej4kw9JuawcLRz/LX/o/R7sY+xPrFufK2VE7kH2X+NWPZ+Opifjzh\n7NrtlX5j+Xj8HAZV0G6Dp93Jx+Pn8Iq33VJCPCc4EcdEGAZMzeCdjGf584DHaD+8D3F+7dbJu3/+\ndO1Y1tXx/dMutRvNU1xMT32E9ya8yvBpgSeL3yzP5KUb/zvk+gGIiCDu8QfZ/+AEskbcTZPr07go\n5TKfIkcX/5PsW/+L7NvHUDD3TWLHBj7/Vabk/Pbpz55lcb/HuPym/yCmne/57ei+g3zxy5fZc5bn\nt/ap3YhPcfFc6iO8O+FVbqqg3b5enskL1W03SsfQX6f+kr9NeIXbKhhDNy/P5H/PZgx1YMwBT3+L\nT3Exo4r+NnzqXbw/YQ4zUh+p1rnHqbEgdWoGC0Y/y18qOEY73pbK8fyjzLtmLBteXczV1ThGnRpz\nbkofxEvTp1a4/LPPv2TPviwWvTmHyY89xJTnZoUcA5ztbyIQ/K3ptwOrgHPZ234FnPVEvO2gnmx5\nexUAWet30CCmCU0SmvmUaZLQjAZRjcjK3A7AlrdX0XZwr5DiXDW4D/98+58AfLv+W5rENCE2Idan\nzInjJ9j8+SYATp86zY4tO2ie1DykOMOGXce8v7wFwBdrMmnarCkuV0JIdQSj9+A+rPDm810F+Zw8\nfoItn28GPPns3LKD+KT4oGPEdW9N4e5cju45QPGpH9jz/mouuc53In9030Hyv96LLT67v84NvL4f\n7735IQAb1m0humkULRLLt33jJo24876RvDD91ZDqj+rehqLdOZzYk4s9dZqD768i7rof+ZQp+PcW\niotOAlCY+R31Q2irEvWu6MDp77+nODsbTp/mxIp/0uDHfcuVa3Ln3Rx782/YkydDjlGi8+Afsead\nTwHYvX4bjaKbENOiWblyu9dvo+BAfrXjXDG4Jxve+QyAfeu30zC6MVF+caJaNKNBdCP2eo/RDe98\nxhWDQ/sjlhP5JHVrTf7uXI7s9fTprxeups0g3+1sO6gHW9725PvtojVcdnXHasVyYmxzdWvN4d25\nHPEeo98sXE1rv3ZvPbgHW9/y5PNdNfNJ7NaaI7tzKfDG2bZgNa384rQa3INvvHG2f7iGS6rZbm0G\n9WSrt92y1++gYQXtVr9Mu22txjnBiTiukv7mbbdvF66mjV+7tTmP9k+HwT1Z7x0L9nrHgugAx+je\n9dtxV/MYrd+pPaf3ZXH6e88YevTjlTRKvdqnjD167MzXplFDqMYnCMd1b43b7/x2sd/57di+gxyp\ngfObE+0G0MWBMdSpMQc87Rb8uWcb4Dn3XFkHx4JE71hQcox+F+AYTamBY9SpMadXt840jYmucPmK\nVasZfv0AjDF07dQBt7uQAwfzQo7jZH8TgSAm4saYKOBq4G68E3FjTKox5hNjzN+NMd8ZY54xxvzM\nGLPGGLPZGNPaW+5yY8xyY8wm7/+XeX/+Z2PMT8rEKCxT70pjzFvGmG+MMX8xHg8BycAKY8yKs0k4\n2hWLO6v0arA7J4/oRN8JZXRiLO6c0gPYnZ1HtMu3TFXiXfEczC69zfhQzkHiXRVPtJrENKH3wKvY\n8K+NIcW5ONnFvr1ZZ77/fl82Fye7ApZ99dXprP1yCRMn/CqkGODJ51B26a03h3IOEVdJPo1jmtBr\nYG82hZBPY1ccx8rsm2PZeTRKCq3dg5WYlEB2Vu6Z73Oy9pPoalGu3K+euI8/vTD/zC39wWrgiuPk\n96XtdTI7j/qVtFfC7QPIX5EZUgyAyObNKT6w/8z3xQcOENHc9w8K9dq0JaJFAidXfx5y/WU1TYwl\nv8z+yc85RFNX3FnVGUhMYhxHysQpyMkjxu/4i3HFUpBdeowWZOcRkxjatjiRT5QrFnd25WNJlCuW\ngixPGftDMSfcx2gUGxVyLCfGNk+M0vULswPEKFPG/lDMyWrk0yRAnCZ+29kkQJyG1Wy3AgfOCU7E\nifJrN3d2HlGJ5fub+yz7m1P7JyYxliNl4gQaC85WvRbNOZ1TOob+sP8AkQnlx+qonw4n+f3Xif3l\nf5H37B9DjtPIFUfR937ntxrOpYRnbCtttyO10G4AzRJjOew3hjar4THUqTEHPMdfVf3N/9xzJMD2\nVBnHgbGgiSuWQr92iwpw7jnbY9SpMacquQcO4Uoo/d0nMaE5uQdCv13cyf52PrHWnvf/6qpgrojf\nBCy21n4H5Bljenh/3hX4JdAZGAW0s9b2Bl4FHvSWmQW8bq3tAvwF+H0Q8brjufp9JdAKuNpa+3sg\nC0iz1qYFlVlFjCn3o3I7KECZUP8CbggUJ3DZiMgIHv3DYyx4bQG5e3JCixNMPsCojAfp3mMgqWk3\n0/fq3owc+ZNyZUJVUceOiIxg7B8e5cPXFpK7JzdgmYACNHt1rjwEFSqIduvQqR2Xp1zK0kXV+NtP\nCH2o+Yhrieramu9feD/0OIEarWwYY4i67xcUvvRCNer2i1QDx0VwcQKFqYVj1IF8ghkHAvfF6gRz\nYGwLZltrIp+A21muUBBlqhcrmHYL+cTuQJzAfTqY7Qg6RIV11Mb+qbFjo/Ig5X8WIEbh3xeQdeNo\nDv/+VZr+/GfViBPgZ7X1y6ET7VZhnBoO5NSYQ3DHT1DHWNWBylfhwFhQfvXaOUZrZcypQqC2Cbht\nVXGwv4lAcB/Wdjsw0/v1G97vPwS+tNZmAxhjdgBLvGU2AyWT5f8AbvF+PQ94Noh4a6y1+7z1bgBa\n4rktvlLGmHuAewBujutN76i2Z5b1GD2Qrrd5Nil7006ik0v/2h3tiqNwv+8tU+6cPKLL/FU3OikO\nd27Vt1UNHT2U626/HoBtm76jeVLpFdZ4V3PycgM/l/3gMw+StTuLBXOCm4jdNyaDu+/2/CKwdu0G\nLrm09PmViy9JIiu7/OQ3K8szwS8sPMrf3niPH/Xqxvz5b1UaZ8jodAbdfh0A2zdtI77MbfPxrngO\n5wa+7ef+Zx4ga3cWH8xZEFQ+JY5l59G4zL5pnBRHUU71b5vz97O7/h+3jroZgE3rvyIpOfHMMldy\nAvtzff962r1XFzp27cCKdQupVy+SuOZxzH/vZUbedG+VsU5kH6L+xaXtVT8pjpMB2qvpNV245Jcj\n2Hrzf2NPng45px8OHiCiRemjCBEtWlB8qDQP07gx9VJSiJ3uOYQj4uJoOuU3HPnvCUF9YNs1owbz\nH7cPAGDPxh00K7N/mrniOZJ7OORtDqT3qEH0vN1zjH6/cSdNy8SJcZU//gqy84hJKj1GY5LiKNhf\n9bY4lU8Jd04e0Um+Y0mhXwx3dh4xyXEU5uRhIiNoEN2Y4/mFQdXv1NhWdlujk0vXj0qKo3B/+Xyi\ny+RTP4R8ShwNEOeoX7sdzfGUOVqNON1HD6SLt91yNu0kJjme773Lgm23wiDazak4Z9b3a7foAPun\n0G//hNLfStTm/rlq1CB+5B0L9m3cSdMycTxjQc0eo6f3H6Bemce5IhNa8EMln6Fy7OMVxI//JaF9\nygoUZefR6GK/81sI+7YqffzarVlyHP/nXda0Btvt2lGD+bF3DP2/jTuIre0xtJbHnKtGDaKXz7nH\nt78V+OVzxO/c0zQpDncQ5x6nx4LC7DyiqjhGC89iDD2znQ6NOVVxJTQnZ3/p7z65+w+S0Dz0x/2c\nOseJlKj0irgxJh7oD7xqjNkNPArciufPaCfKFC0u830xFU/wS/5mdLoktvH8yap+mTJl6/2hkrp8\nK7Z2trW2l7W2V9lJOEDm68t4LX0ir6VPZNuSdXQa4XmGNrl7a064j3HUbwA8uj+fk0ePk9y9NQCd\nRvRl29J1VW7Dh69/yENDHuShIQ/y+cer6T+iPwDtu7fnmPsohwMM1iPHjaJxdBNemTw7mDQBePGl\nuWc+dG3Bgo8Z9TPP1e2reveg4EgBOWVuswOIjIwkPt5za029evUYOnQgW7dWPQn76PVFPDLklzwy\n5Jd88fFq0rz5tOvenmPuYwHz+c9xI2kc3YQ/TX4l6HxK5G3YSXSKiyaXtiDiokguu7EP+5ZU3e7B\n+suf/sHwtP9keNp/suyjldx061AAuvXshLugkAN+E/G//vkt+na+nrSew7jthrvZveP/gpqEAxRu\n2E6jlCQaXJqAuagezW/sS97Ha33KNOmUQutn7+WbjGc4daigWjmd/uYb6l18CREuF9SrR4O0/pz4\n97/OLLdHj3Lwlhs59LPbOPSz2zj11VdBT8IBPpu3hGfTH+fZ9MfZtORLet9yLQAtu7fluPvYWT0L\nXtaaeUt5MX0CL6ZP4Jsla+l2yzUAXNK9DcfdRRT6xSk8kM/JwiIu6e75VNRut1zDN0H0FafyKZG9\ncSexKS6aevt0h2F92L7U9xGE7csy6TTCk2/79N7s+fdXQdfv1NhWIscvnyuG9WGHXz47lmbS8See\nfNql92ZvCPmUyN24k6YtXUR747Qd3oddfnF2Lc3kCm+cNkN7s+9fwcdZ//oy5qZPZK633Tp62y2p\ninZL8rZbxxF92R5EuzkVp0TOxp00S3ER42239kHsn1D6W4na3D9fzFvKrPQJzEqfwNdL1tLdOxZc\n2r0NJ9xFZ/VMcyAnt35LvUsvpl6yZwxtcl0qRZ/4flhavUtLP1St0TVXcWrvvpDjBDq/ff9xzZ3f\nVs9byh/SJ/CH9Al85ddux2uw3T6dt4Rn0h/nmQBjaFEtjKG1PeZ8MW8pf0yfwB+97Vb23HOignPP\nCb9zz9dBnHucHgtyN+6kWcvSsaBdDY+hJZwac6qS2rcPCxYvx1rLxi1fExXVhBbNQ39MwqlznEgJ\nU9mtLsaYe4Ee1tp7y/zsE2AZcJW19gbvz1YC46y1a40xqd6vbzDGLAD+Ya2dZ4y5A7jRWnuzMeZJ\nINpa+7gx5ibgXWutKbuut95ZwFpr7Z+NMZuB4dba8u/G8PPM5SMrvUlk0JQMWvXrwqmikywaN5uc\nzZ4q71w0jdfSPZ/s6eqcwtDn7/G8NmLlRpY+Vf71FKts5X8FHTPlPnqmel73NXPcDLZv8nzoxu8/\n+gMPDXmQeFc8c9e8zt5tezl18hQAH8xdyJI3lvjUszhnQ6Vxfv+7aVw3OJVjRUX8/OePsC7T8wFw\na79cQq8fDaZx40as+Oc7XHRRPSIjI1m+/DPGPfpriouLfeoZ5uoRqPoz7pkyhu6pPTyvLxv3O3Z4\n85n+0e94ZMgviXfF8+qaP7OvTD6L5n7IMr98fno6psIYSf270uPX3te7vPEJX/3+fTo/OoK8jbv4\nfkkmcV1bcc2ch6nfrDE/HD/F8QNHWJT2eLl6njpV8avISkz6n8e5Nu3HFBUd54mHJrNl49cALFjx\nV4an/adP2YsvTWL2X2aWe33Z3HotK6y/Wf8epDx9JyYygtw3/sn3v3ubSx+9jcKN2zm8ZC1XvjmJ\nxh0u45T3r9Qnvj/IN3c8E7CuNldW/KxT/d5XEfWLBzERERR9tIhjf51Pkzvu4v+3d9/xUVX5/8df\nn4QgvSSUgA1QrEgTEUGB0NS4VtauoO6q2EWK7ftbC+IiNhRcdRUVXVfdXVcXJSJYwIqAdCtVEAi9\nJBASyvn9cW9gMplMMuFmCMn76SMPk5nhvOfcc+bce+a2nb/8TN63BTco6z05iuwXny9yIv7I4sjX\nF8h38cPXcXy3NuTl5PHmkOdZMX8JAEMzHmNkutcO591zJR3O70KdxvXZumYT377zGR+NKnj0Rb1i\nvl875+FraOl/Rt8b8iKr/M/oTRmP8nz6fQA0Pak5F/q3kFk4ZS4THih8q7zNRD/KIKj6HOaSisxo\nkdaGHn+5yrsVyr+mMm3MeE6/qy+Z85ay6JNZJB6SxDlPD6Dxic3YsTmb8beOYcuKyLex2xPx0X2C\nGtuqRBlBm6e1Ie2Bq0hITGD+O1P5bsx4utzVl8z5S1k82atP+qgBNPLr8+GtY9iyPHJ9qkXJOTKt\nDWc86C23H9+Zyvejx9NxUF/WzlvKMj+n96gBNGjVjNzN2Xx8yxi2FpGzvZiTsHoN60/zbq3ZlZPH\nRyHLrX/GcMaFLLez/eW2dMpcPomw3IoTRE5iMYdANk9rQ3e/fRb47dP5rr6sCWmfs0PaZ0IR7XNI\nMTlBtc/qxOi9+tyHr6FltzbszMnlv0NeZKW/zG7NeJQx/lhw5j2X0+b8zt55tWs2MfOdKXw26t0C\n5dyQvLZQ2fmqdeno3b4sIYHs8RPZOvaf1B3Qn7wffyXni2+pP/hmqp3aHnbtYs/WbDY+NpqdS36L\nWNa01Y0jPg7e+m3v7cvenspPz/yPVv76bZW/fuvySsH128TuhddvAHOKaaDzHr6GY/zl9p+Q5XZb\nxqOM9pfbWfdcTtuQ5TbjnSl8GrbcstgdNecSfwzdmZPHP4Y8z3J/DL0n4zFG+GPo+f4YWrdxfbb4\nY2hG2BjabE/RY2iQY87mhOj97Q/+csvz+1v+uueWjEd5LmTd0/eJAd7ty6bM5cMHXitUTh0XfdAJ\nasypHqU6+Z/RBP8zOnP0eE71P6NLQz6jDf3P6MQoY2helKO8gxpzAG6f9XDEx4c8MIIZs+exefNW\nUpLrcfOfrmbXLm/9fumF5+CcY/hTf+OraTOpXq0aw+4bSKvjj4lY1jPt/1J0ZQi2vw1e/o9SHB9f\n/iTXbnnQH3y/MWthuWyL4ibiU4ARzrmJIY/dDtwELC7BRLwZ8ArQAFgHXOucW25mjYH/4e0V/xS4\nzTlXq5iJ+G3ALcDq4s4TL24iHpTiJuJBKW4iHpTiJuJBiTYRD0pJJuJBiDYRD1K0iXiQipuIB6W4\niXhQipuIByXaRDxIxU3EgxJtIh6kaBPxIBU3ET+YFDcRD0pxE/GgFDcRD0q0iXiQok3Eg1TcRDwo\nxU3EgxJtIh6k4ibiQSluIh6UaBPxIEWbiAepqIl4kIqbiAdJE/Hyo7xOxKNuDTvnukd47FnCLroW\n+jrn3BRgiv/7MrxD28PLWAN0Cnno3vB/6/99a8jvo4HR0d6viIiIiIiISHlXgfYTiIiIiIiIiJR/\n8Tk+VERERERERA4q5fk+3Ac77REXERERERERiSNNxEVERERERETiSBNxERERERERkTjSOeIiIiIi\nIiJSyB50jnhZ0R5xERERERERkTjSRFxEREREREQkjjQRFxEREREREYkjnSMuIiIiIiIiheg+4mVH\ne8RFRERERERE4sgq4rccfz3yqrhUarXtjEcMp+YlxSXn/aSsuORclVuzzDM+q7anzDMA6pIYl5xf\n3La45HR2teKSsyRhV1xylu3JjkvOjbnxWW5PVd0Ul5w+CQ3jkvPerpVxybmwyqFxyZnO1jLPWJYX\nnz7Q6ZCmcck5bnd81m+7LS4xJMVpkyq7gu1Gic8aG6pUsE3e+GyBxK994tE8d8x6OA4pnqQGLeI0\n8pStOjVbHPSfnK3blpTLtqhgQ7mIiIiIiIhI+aZzxEVERERERKSQPRXw6OnyQnvERUREREREROJI\nE3ERERERERGRONKh6SIiIiIiIlKIi8tl9Con7REXERERERERiSNNxEVERERERETiSBNxERERERER\nkTjSOeIiIiIiIiJSiG5fVna0R1xEREREREQkjirlHvHeD17NUWlt2ZmTy4eD/86aBcsKvSa1VTPO\nefJGkqpVZfHnc5j84Bsx5/R94BpOSGtHXk4ubw5+nt9/WFroNecMvpSOF3WlRt1aDDmxf8wZTbq3\npsOwq7GEBBa9NYUfx3xQ4PlGpx7LyQ9fTb3jD+erm8awYsKMmDPyXfvg9bRPO5ncnFyeG/wMSxcs\nKfB81WpVGfT83TQ+IpU9e/bw/SczePOx12PKaJTWmpOG9YPEBJa/+TkLw+qT0uk4Wj18NXVOOIKZ\nA0az+sPppa7PRQ/054S0duzc2z7LCr3mnMGXcspFXalRtyZDT7ymVDnpD/SjZVobdubk8d7gF1kd\nIadJq2Zc9MQAqlRLYuHnc8l4KLblds2Df6ad3zbPD342YtsMfH5ogbZ567HY+vSR3VrT7cGrscQE\nfnh7CjP/VrBtEqtWoc/TA2h0UnN2bMoi45YxZP2+PqaMfPFqm+sfuoGT0zqQm5PLM4NGsWTB4gLP\nV612CHc/fw+pR3rLbcYn03l9xLiYMhqkteGER/pjiQmsePMzloweX+D55jemc9iVPXC7d5O3IYt5\nd77AjlIut1seuomOPTqSm7ODkXc9yaIFiwq95q9vDCe5UTKJiYnMn76A0f83hj179sSU0/PBq2nh\nj6EfFTGGNm7VjPQnb6RKtaos+XwOn5ZiDB348G107nEqO3J2MGzgY/y6YGGh1zz9j8dIaZxCYmIi\nc6fP44n7nim39bnuwetpl9aBvJxcxgweVeQYmnpEE/bs2cPMT6bHPIYCDB52B116dmJHTi4P3vko\nv8z/tdBrnv3nEzRolEJilUTmfDeXx+59Oubl9scHruFEfx33RhHruHND1nGDSrGOO6J7a87wx50f\n35rCrLBxJ6FqFXqPGkBDf9z5+ObYx514jW2Hd2/N6Q9eTYJfl9kR6tIrpC6TSlEXgObdWtPzAa8+\n896ewnfPF67POU8NoPFJzcnZlMX4W8ewtZRjTrw+O71CttsmRMkJ3W77JMacZt1a08PvB/PfnsL0\nCP3g7Ke95bZjUxYf3FK65RavnCO7taa7398WvD2FGRFyznx6Xz/I2I/6pPn1WRClPvmfnw/L8XL7\nv0ef4ouvp5Ncvx7v/+OFQs875/jrqBf48tsZVKt2CMPvH8QJxx4dc11E8pXJHnEzSzWzt81ssZn9\naGYZZnaMmS0oi7xYHJXWhvrNU3mh2yA+uncsZz1yTcTXnTn8WibeO5YXug2ifvNUWnRvHVPOCd3b\n0rB5KsO638E7973EJcP/FPF1P3w6iyfPvz/WagBgCcYpj/bn8ytH8mH3oTQ7vxN1WjYt8JptKzfw\n7Z0vsuy9b0qVka9d2sk0ad6E27oN4MV7n+P6R26K+Lrxf3+fO3vewtD0gRzb4Tjadm9f8pAEo/Vf\nr+XbK0byWdchHHphZ2ofc2iBl2xfuZ7Zd7zAyv2sj9c+TXik+528fd9LXDz8zxFft+DT73mqlO0D\n0LJ7G1Kap/JM90GMv28s5w6/NuLrzn3kOsbf9zLPdB9ESvNUWnZvU+KMtmknk9q8CXd0u4mX7v0b\nf3pkQMTXffj397mr563cnX4Xx3Y4Pqa2sQSj+yP9eb//SN7oOZRjzutEclhfO/HS7uRu2ca4roOY\n/fJETr/3shKXHypebXNyWgeaNGvKgK438Nw9Y7hp+M0RX/f+3//LLT1uYuDZd3BchxNo3/3kkock\nGCeOuI4ZV4zgizMG0fTCLtQK69NbFizj6zPv46u0u8n84DuO+8uVpapPx7RTOLT5ofQ/41qevvsZ\n7nj0toivG3bTcG488yb+3OsG6qXUpesfzogpp4U/hr7UbRAf3zuW3kWMoX2GX8vH947lJX8MbR7j\nGHpaj1M5vPmhXHz6VYy4+0mG/nVgxNfdP+Ah+vX+M1f2uJZ6yfXo8Ydu5bI+3hjalNu63cgL9z7H\nDVHG0Dt63syQ9Ds5rsPxtItlDAW69OjE4S0O48LOlzN8yEjuHTEo4uvuveEvXNHrWi7t3o/6KfXo\ndW5aTDn567iHut/BW/e9xGVFrOPmfzqLx/djHdftkf580G8k/+wxlGPO70T9sHHnhMu6k7t5G/84\nYxBzX55I5/tiG3fiNbZZgtH1kf5M6DeSt3oMpWWEuhzv1+VNvy6nxViX/Jxew/rz7/4jGdtrKMef\n14mUsJyTLu3Oji3beKnbIGaOnUj3e0o3Vsfrs5Of82K3QUy8dyxnFrPd9mIpttsswej1SH/e7T+S\nV3sO5bgoy21s10HMfHkiXUvZD+KV08Pv1+N6DuXYKP361a6DmLUf/brnI/35b/+RvFZETiu/Pq90\nHcT35Xy5XZDemxeeeqTI57/8dgbLf19FxjtjeXDo7Qx7YkzMGSKhAp+Im5kB7wFTnHNHOedOAO4D\nGgedVRote5/Mgne/AmDV7MUcUqcmNRvVK/Camo3qcUit6qyc5e1NWvDuVxzTp0NMOSf1OYXp//0C\ngGWzF1K9dk3qNKxX6HXLZi9k67rNpakKKe2OImvZGrKXr2PPzt389r9pHH5mwUnCtt/Xs/mnFbg9\n+3d+xym9OzL13c8BWDj7V2rWqUm9RvULvCZvRx4/fDsfgF07d7F0wRJSUlNKnFG/3dFsW7qG7cvX\n4nbuZuX735IaVp+cFevZ+tMKXIx7bsK16tOBGX77/DZ7EdVr14jYPr/NXlTq9gE4rs/JzPnvlwD8\nPnsR1WrXoFZYTq2G9TikdnVW+P1tzn+/5Lg+JZ/sndK7I1+8OwUorm2878F279zF0gWLSY6hbRq3\nPYoty9aw1e9rv34wjRZh77FFn/b8+B+vrgszpnN4lxNLXNCIkaoAACAASURBVH6oeLVNxz6n8vm7\nnwHw6+xfqFmnJvULLbdc5of06SULFpPSpEGJM+q1P5rtSzPJ+c3r06vf/4bGZxUcSzZ+/SN7cvIA\n2Pz9Qqo1SS5VfTr3OY3J734CwE+zf6ZWnZokNypc1vbs7QAkVkmkSlIVYr096NG9T+YHfwxdPXsx\n1YoYQ6vWqs4qv0//8O5XtIxxDO16Zhc++s8k79/P+oladWuSUkx9kqpWiflup/Gqzym9T2XK3jH0\nF2qUYAxdsmAxKakl728A3c46nYx/TwRgwawfqV2nFimNCn/WtxXoB0m4GM8BbB2HdVz4uLNwfORx\n52d/3Fk0YTqHxTjuxGtsaxSWs2j8NJqH5TQPqcviCdM5tBQ5TdoexeZla9iywsv56YNpHN27YE7L\n3u1Z8K6X80vGdI4o5Vgdr89OLNttq0K222LJSW17FJuWrWGL3z4/fzCNo8La56g+7fnBb59fS7nc\n4pmzOSTnlyJyQvt1UDlHh+UcfRAttw5tT6JundpFPv/5V9M476yemBltWh1PVlY269ZvjDnnYOOc\nO+h/yquy2COeBux0zu09psM5NwdYkf+3mTUzsy/NbJb/09l/vImZfWFmc8xsgZmdYWaJZvaa//d8\nM4u8W6SEaqfWZ+uqDXv/zsrcSO3GBTeGajeuz9bMfR+sras3Uju14GuKU7dxfTaH5GzO3EDd1NJt\nYBelemp9tq/a9z63r95I9Saxvc+SSk5NYcOqfYf4bMhcT3LjoidyNerU5ORepzD/63klzqjWpD45\nIcssZ/XGUk9KilOvcXKB9tmSuTHw9gGo0ziZLSE5WzM3UiesL9VJrc/W1QX7W53GJX8v9VOTw9pm\nA8lR/n1+2yyIoW1qpdYnK6SvZa/eSK2wz03N1Ppk+69xu/eQm7WdavVrlTgjX7zaJiU1hfWr9y23\n9Zkbon5xVLNOTU7p1ZF5X88pcUa11GR2hPbpVRs5JEpdDrsijXWflbz8UA1SG7Bu1bq9f69bvZ4G\nRdRnxD+G85/Z75CzLYcvJnwZU05Jx9CskDE0qxRjaMPUBqxZtXbv3+tWr6dhEZPSp98cScbc99ie\nncPnH06NKSde9UlJTWFDSPtszNxASjFjaIdeHZn39dyYchqmNiQzZLmtWb2ORkV8eTT6rSeZPP8D\ntmdv59MPp8SUU69xfTaFrePqBfw5rRlh3KmZWnjcyQoZd/JiHHfiNbaFlhGtLtn7UZe99Vkdva/W\nSq3P1rD6VC/FWB2vz07t1PpklXFO7Qj9oFBGhL4W63KLV05J+nWtsJzS9OvwnKwS5pTX5VacNes2\nkNpo33jauFED1qwr3WkdIlA2E/FWwPfFvGYt0Ns51x64FHjWf/wK4GPnXFugDTAHaAsc6pxr5Zw7\nCXh1v96dWeHHwr4psQivifXblEhlhOfsr8gZgUYUkxU5LCExgTtHDyLj1Q9Zu2JNmWTst4hRwWdF\nrpIryYtiyIjUXyO/NiExgdtH38XEVyfE1DaR3mPhagTUH+PVNhGCispJSExg0OghfPjqeNYsj2W5\nRXowckbTvqdTt20Llj73QcTni42KYbndc9X9XNLhcpKqJtG2S9v9DipJn465DWPo1wOvHMq57fuS\nVDWJk7u0CyCnLOpT+KFo/W3g6MExj6EQ2/rrtssHcVbbC6h6SBKnnB7bIfCBLJNSZBT++OznuBOn\nsa0k43TkMSnGnBKUEcs6I3rYgRwLgl2Plmi8CWK5laOcsurXhco4mJZbMSL13YjLQKSEDtTF2pKA\nMWbWFtgNHOM/PgN4xcySgPedc3PMbAnQwsxGAxOASZEKNLMbgBsALkjuSMdaLfc+175fL9pe5p3/\ntnreEuo03bcXonZqMllrCx425+213PfNfp0myWSvKf7QujOu7sNpl/cEYPncxdQLyamXmsKWNZuK\nLSMW21dvpEbTfe+zRpNkcjKDyzizXzq9LusNwKJ5i0hpuu9bwJTUBmxcG/lwnBtH3MLqpavJeCW2\nCUXOqo1UD1lm1ZsksyPA+px+dR9Ou7wHULh96qYmszWg9ul4dW9OvtzrbyvnLqFuSE6d1GSywvrS\n1tUbqdOkYH/bujb6e+nT72x6XtYHgMXzFoa1TQqbimibG0bcTGYp2iZ79UZqh/S1Wk2S2Rb2HrNX\nb6RW02SyMzdiiQkcUrsGOzZnl6j8eLVNer9z6H35mQAsmreQBiF7ChukprBxTeTldsuI21i9bBUf\njB0f8fmi7Fi9kWqhfbppMrkR+nRK11YcfeeFTLvwIfbk7Spx+ef1P5f0y88G4Ne5v9KwacO9zzVs\n0oANRdQHYGfuTr6Z/C2d+5zGrC9nRc1p168Xrf0xNNMfQ1f6z9VOTSY7bAzNytxI7ZAxtHYJx9C+\n/S/gvCvPAeCnOT/TuGmjAvVZv6boPQ95uTv5avI3dD2zCzO+jP5dcLzqc1a/9LDPaUPgJ8A7yqio\nMXTAiFtZvXQVE14pWX+7+JoLueDKcwH4ce7PpDZtRP5+9MZNGrIuc0OR/zYvN4+pH39NtzNP57sv\nZkbN6Xp1Hzr767jf5i6mfhmv47ZFGnfCMrZleq/Z5o87VWMYd6Dsx7bwMkJztofVJTvTe01oXXJj\nzMnK3EjtJuF9tWBO1uqN1CllfeL12WnfrxdtQrbbaodtt5UkJ3xdG01WhH6Qvbbwcqsdstxi7Wvx\nzClJvw7PKU2/Dq9P7Qj1yS6DnLJabsVJbdSAzLX71kNr1q6nUYOSn+YnEq4s9oj/ABR3gutAYA3e\nXu8OQFUA59wXQFdgJfCGmfVzzm3yXzcFuAV4OVKBzrm/O+c6OOc6hE7CAWa9/gmvpN/PK+n38+uk\n72nV93QAmrY7itys7WwLG9C3rd1M3rYdNG13FACt+p7OwsnF7eSHL9+YxMj0uxmZfjfzJs2g40Vd\nAWjWriU7srbv1/mskWyYs4TazVOpeXhDEpISOfL8Tvw+KfoGdSw+fj2DIekDGZI+kBmTptGtr7dS\nbNnuGLZnbWNzhMniZYOvpEbtGrz2UMRmimrznMXUbJFKjSMaYkmJHHrBaWROKn65l9RXb0zi8fR7\neDz9HuZPmskpfvsc2e7oQNtn+huTeT79Pp5Pv4+fJ82k7UXexbAOa3c0O7JyyA7LyV63mbzsHA5r\n5115s+1FZ/BzMfWe9PpH3J0+kLvTBzJj0nd07dsdiN42lw6+ghq1azLuobEx12nN3CXUa55KHb+v\nHXNuJ5ZMLtjXlkyexQl/9OraMr0jK775scTlx6ttMl6fwMCzb2fg2bcz7eNvSevrTf6PaXcs27K2\nsynCcrty8FXUqF2Dlx98Kea8LbO9Pl3d79NNLujMmo8Ltm2dVs1o9fj1zOz3OHnrt8ZU/vhxHzDg\nrJsZcNbNfP3xN/Tu2wuA49sdx7as7YUmetVqVNt73nhCYgKn9ujIikUrCpUbbvbrnzAu/X7Gpd/P\nwknfc6I/hjYpZgxt4o+hJ/Y9nUUlGEPfHfc+/ftcT/8+1/PFx19z9h+9SeyJ7Y9n29ZtbAirT/Ua\n1faeN56YmMBpPU7lt0XLy019Jr6ewZD0OxmSfifTJ31H971j6LFsz9oedQx9NYYx9N+vvceVva/j\nyt7XMeWjL0m/+CwAWrU/geysbDasLTgRr16j+t7zxhMTE+nSsxPLSrDcvnhjEiPS72ZEhHVcThms\n49bMXULdZqnU9sedlud1YmnYuLN08iyO88edo8/pyO9fl3zcyc8oy7Et39qwuhwdoS7LQupy1Dkd\nWRljXQBWz11C/eap1PVzjj+3E4vCchZ9MotWfb2cY9M7sjyG+sTrszPr9U94Nf1+XvVzymq7LV9m\n2HI77txOLA5bbosnz+JEv32OKWU/iHdOfr8+toz6dWbY5+fYEtQnlv4WXp+yXm7F6X56J8ZP/BTn\nHHMX/EStWjVp2KBsTqEsT1wF+K+8sqAPJfMv1jYNeNk595L/2ClADeA551wrM3sa+N0596SZXQu8\n4pwzMzsSWOmc22VmdwLNgEeAPOfcVn8P+mv+oetF+uuRV0WtVJ9h/WnRrTU7c/KYMPjvZM73brly\nXcZwXkn3ru6aelJz/vDkDd7tNqbMZdJfCt9CZrXtjLosLn74Oo7v1oa8nDzeHPI8K+Z7t6oZmvEY\nI9PvBuC8e66kw/ldqNO4PlvXbOLbdz7jo1H/KVDOqXlJRWY07dGGkx+6CktMYPHbU/nh2fG0HtKX\nDXOXsnLSLJLbtKDb2DupWq8Gu3fsJGfdFiak3ROxrPeTsqLW50/DbqRtN+9WNc8NHs2S+d5FUR7P\neJoh6QNJTk3hxe9e4fdFK9iV6y2bj17P4LO3Jxco56rcmkVmNOrZlpMe9m5PsfytKfz6zP84bugf\n2TxnCZmTZlGvbQs6vjKQpHo12bNjJzvWbeHzbkMLlfNZteIv5vbHh6/l+G5tycvJ5Z9DXtjbPkMy\nRvB4ureMzrvnCk4u0D6fMzGkfeqSWGzOOQ9fQ0u/v7035EVW+f3tpoxHeT79PgCantScC5/wbruy\ncMpcJjxQ8BZZv7htUTOuG3YDbbq1J8+/fdmS+d5tuB7LeJq7/bZ5/ruxrFy0gp253h7Xj1+fwGdv\nf1KgnM6u6POpmqW1oesDXl/78Z2pzBgznk539WXN/KUsnTyLxEOSOHPUABqe2Iwdm7P56NYxbF2+\nLmJZSxKi7/UNom0Alu2J/m34jcMG0K67d9u30YNHsWie16ef/uhZBp59OympKbwyfRwrFq5gZ57X\npzPGfcjktwsemHNjbtHLrWHPtpwwrD8kJvD7W5+zeNT7tBx6MVvmLmHtx9/T8d/3U/v4w8n1997k\nrFzP9/2eiFjWU1Wj73G87ZFbOKW7dzu2xwc9ya/zvNt9vTDxbww462bqNajH8NceJqlqEgkJicz5\nZg5/e+gF9uwu+Hnpk9AwUvF79RrWn+bdWrMrJ4+PQsbQ/hnDGRcyhp7tj6FLp8zlkwhj6Hu7VhZ6\nLNTg4XdwavdTyM3J5ZG7HuPned5tuMZNeon+fa6nfoP6PDHuUapWTSIhMZHvv57FMw8+x+6w+lxY\n5dBIxQden+lE/yLlz8NupG239uTm5PK3wc+yeO8YOooh6XeSnJrC3797ld8XrWCnP4ZOfH0Cn4aM\nocvyit/rPPTRgXRO82779tDAv/LT3F8AeHPyK1zZ+zqSG9Tn6Tceo2rVqiQkJjDzq1k89cBodu/e\nvbeMToc0Lar4vS7x13E7c/L4x5DnWe5/Tu/JeIwR/jrufH8dV7dxfbb467iMkM/pcbuLXr8BHJnW\nhjMe3DfufD96PB0H9WXtvKUs88ed3qMG0KBVM3I3Z/PxLZHHnd1Rjh4NcmxLirL1cURaG0736/Kz\nX5dTBvVlXUhdeo4aQMNWXs7kIuoCkB1lN0qLtDb0+IuXM/9fU5k2Zjyn39WXzHlLWfSJl3PO0wNo\n7Ndn/K1j2LIick5xgvrsFLfG7h2y3ZYRknNtxnBeDck5J2S7bXKEnCpR2qd5WhvSHriKhMQE5r8z\nle/GjKfLXX3JnL+UxX77pI8aQCN/uX146xi2FNE+0QSZE20LpFlaG7r7/fqHd6Yyfcx4TvP79RI/\n56yQnIwoOdHap7mfk5CYwAK/Pp39nPz6nB2SMyFKTrSN96CW2x2zHi4yY8gDI5gxex6bN28lJbke\nN//panbt8rZXLr3wHJxzDH/qb3w1bSbVq1Vj2H0DaXX8MUWWl9SgRYU4bv2QaoeX35lsCeXuWFEu\n2yLwiTiAmTUFRuHtGd8BLAPuBN7zJ+ItgXeB7cDnwG3OuVpm1h8YAuwEsoF+QB2888LzVzv3Ouc+\nipZf3EQ8KMVNxIMSbSIepOIm4kGJNhEPSkkm4kEoyUQ8CMVNxIMSbSIepOIm4kEpbiIelGgT8SAV\nNxEPSnET8aAUNxEPSnET8aAUNxEPQkkm4kEoyUQ8CMVNxIMSbSIepGgT8SBFm4gfjOKzxo4+ET8Y\nxWcLJH7tE4/miTYRD5om4uVHeZ2Il8k54s65VcAlEZ5q5T+/EAi9weO9/uPjgHER/l2MV5ERERER\nERERKZ8O1MXaREREREREpBwrz/fhPthVsIObRERERERERMo3TcRFRERERERE4kgTcREREREREZE4\n0jniIiIiIiIiUojOES872iMuIiIiIiIiEkeaiIuIiIiIiIjEkQ5NFxERERERkUJ0YHrZ0R5xERER\nERERkTjSRFxEREREREQkjjQRFxEREREREYkn55x+vMvy36Ac5ShHORWpLspRjnKUE++cilQX5Sgn\n3jn6qVw/2iO+zw3KUY5ylBOnDOUoRznKqag5FakuylFOvHOkEtFEXERERERERCSONBEXERERERER\niSNNxPf5u3KUoxzlxClDOcpRjnIqak5FqotylBPvHKlEzDndpl1EREREREQkXrRHXERERERERCSO\nKv1E3MxqHuj3ICIiIiIiIpVHpZ2Im1lnM/sR+Mn/u42Z/e0Avy0RERERERGp4CrtRBx4GjgT2ADg\nnJsLdI1HsJn1Dri8OmZ2VITHWweck2pmqf7vDc3sIjM7MciMInIfjUNGc78+xwVc7hFmVs3/3czs\nWjMbbWY3mVmVAHPOy88pa2bW1cyO9X8/3cwGm9k5ZZBTy8z+aGYDzew2MzvLzAIds8ysipndaGYT\nzWyemc01s4/MbICZJQWZVUR+YBd/MbNEvy7DzKxL2HP/F2BODTMbamZDzKyamV1jZuPNbKSZ1Qoq\np4jsX8ugzNYhvyeZ2f/59XnUzGoEmHOrmTXwfz/azL4ws81m9p2ZnRRgzn/N7Ko4tEULM3vFzB7x\nP6svmdkCM/u3mTULMCfBzK4zswn+5/N7M3vbzLoHleHnHNCxwH8PGg9KnquxoPicCjUWhOR9WpLH\nAsi5w7ztazOzsWY2y8z6BJ0jlVulvVibmX3nnDvVzGY759r5j811zrWJQ/Zy59wRAZV1CTAKWAsk\nAdc452b4z81yzrUPKOdG4B7AgMeAa4AfgC7ASOfc2IByng1/CLgaeB3AOXd7QDnvO+cu8H8/H28Z\nTgE6A391zr0WUM4CoKNzbruZPQYcBbwP9ABwzl0XUE4OsA34CHgL+Ng5tzuIssNyRgEdgSrAx0BP\nP7MbMNs5NySgnEuAIcBcIA34Bu+Lw5OAK51z8wPKeQvYDIwDfvcfPgzoDyQ75y4NICO5qKeAuc65\nw/Y3w895GagBTMf7zEx1zt3lPxfkWPAvYAVQHTgW76iifwHnAqnOuasDyskC8ldQ5v+/BrAdcM65\nOgHl7F02ZvYkkAK8ClwApDjn+gWU84Nz7kT/9wnAy8659/wJ5XDnXJeoBZQ8ZyXwLd4Y8wneeDDB\nOZcXRPkhOV/4ZdcFrsJbZv8C+uB9RnsElPMq8BteXf4IbAW+BO4G/uecGx1QTpmPBX6OxoPYMzQW\nlC6noo0F1fDa/XOgO/v6Qh3gI+fc8UHkhOTNdc61MbMzgVuA/we8GtRnRwQA51yl/AH+gzfpmgVU\nBQYDbwdY/vgifj4AtgWYMwdo4v/eEfgZuMj/e3aAOfPxBsAUIBtvBQtQH5gTYM7vwD+AfngbQP2B\ndfm/B5gzO+T3b4Dm/u8N8DaGgsr5MeT374GEkL+DzJntt8X1wKfAGuAFoFtQGX7OD3grvxrAJqCG\n/3gSsCDAnHkhZTfA+2IBoDXwTYA5v0R57teAMnYDS4ClIT/5f+cFucxCfq+Cd6uV/wKHBDwWzPH/\nb0Am+77QtdD3EEDOaLwv4BqHPLY0qPJDygwdC+YASWVUn19Cfp9RVNsFVR+gNt4ELMMfQ18F+pTR\nclte1HMB5MwL+3ua//9DgJ/Kon0iPBfIWOCXpfEg9gyNBftRnwo0Ftzhf05ywz5Dc4Fby6A/zPP/\n/wxwYdD10Y9+nHMEdmjsQWgA3ofrULzJ3yS8b7yCcgbeN4PZYY8b3oQ5KFWcc6sBnHPTzSwN+NDM\nDmPfN8hB2OWc2w5sN7PFzrlMP3OTmQWZcwLwMHAWMMQ5t9LMHnDOjQswAwoumyrOuaUAzrn1ZrYn\nwJwVZtbDOfcZsAw4HPjNzFICzABvr8Am4CXgJfNOIbgEGGFmhznnDg8wx4Uso/zluIdgT3UxIMf/\nfRvQyA+fZ2aB7P3wbTKzi4F3nXN7wDsUFrgY74uGICwBejrnloc/YWYrAsoA7wtFAJxzu4AbzOwv\nwGdA4Icm+v0gwznnQv4ObCxwzt1mZicDb5nZ+8AYgh3T8tU1swvx+u8hzrmdfn6g9QH+Y2av4Y1v\n75nZnXgTo55Aob6xH/LbIwt4A3jD3wt7Cd5RTZMCytljZsfg7QWrYWYdnHMzzexoIDGgDICdZnaU\nc26xmbUH8gCcc7kBt088xgLQeFCasjUWlE6FGgucc88Az5jZbS6gI2GK8b2ZTQKaA/eaWW28bR2R\nwFTaibhzbj1wZRlGTAO2O+emhj9hZr8EmLM1fyMFwDm32j+86X0gyPO3d5tZkr9i2ns+sH+oUGAT\nMOfcVuBOf6X7D/+wrbK4lkFrM9uKN+GrZmapzrlMM6tKsBuRfwZeN7MHgS3AHDPL33t9V4A5Bfhf\nlDwLPGtmRwZY9AQz+wpvr8rLwL/MbBreoelfBJkDTDSzqcDZwL9h72GdFu0fxugyvFMt/mZm+Rvb\n9fAOfbssoIxReO0daQNrZEAZADPN7Czn3MT8B5xzD5vZKuD5gHNqOeeyXcipFeZdpyIrwBycc9+b\nWS/gVmAqUBbXQZgKnOf/Ps3MGjvn1vhfZq0PKsQ5d7+ZXYN3COdReJ+hG/DG6iDXReFf/uKc24h3\nhMwLAeYMxTvCaw/eobv3mlkbvMNErw8wZwjwuZntwDvy5jLwrlMCfBhgTjzGAtB4UCoaC0qloo0F\nADjnRptZZ6AZIfMY59zrAUf9CWgLLHHe6YXJwLUBZ0glV5nPEW8O3EbhD/J5Rf2bGMt/Dvinc+7r\nIMqLkjMBGOGc+zLs8STgEufcmwHlvAK84pz7KuzxQ4HjnXOfBJQzBm+5fWNmBtwMnOacuyqI8kNy\nIraPmdXDq8+3AeWMwVvZbgJa4vW13/EORwvsm1Xz7gDwZ+fcN0GVWUTOc8DbeIdQfudvbF2It1H5\nn6Dq5Odk4p0DODe/f/l7qJKcc7lB5IRlpuCNiYFtcFU2ZmaujFYqZtYEaOecyyiL8mX/mXcBqk0u\n4OtT+OuClHh9NjUWBKOsxgONBeVfWY0Fftlv4H2BMQfvVA/wDlwI5BpCITld8E692GZmVwHtgWec\nc78FmSOVW2W+avr7eIcKjwaeDPkJyq/AE2a2zMweM7O2AZYdahIwMjzHObczqEm4by7weISclUFN\nwn0LgSfNbBkwAvg66Em4L2L7OOc2BzUJ9y0EnsA7N6szsNg5912Qk3Dfi/jLrYz72694e23eMe/i\nc7Wdc0845/4VcJ1+BdKB24HeIe2zpywm4X7ZG0I3vC3guxtEEo+MeOYAvcqqYOfc6vwN74q23CpK\njnNuvXNud9A5zlNoUhx0jvl3IIkwFgR9B5J43enkgObgXVwz8IywsaBCLbOKkhMyFgSa4+sAdHHO\n3eycu83/CXQS7nse73TMNnh7/X/Dv3CwSFAq8x7x75xzp8Yh50i8w9ouwzuU6i28i8IFeuuNInLe\ncs4tjENOvOqjHOWUSU4R2YHd3eBAZihHOcopUVnxugOJcsphhnLKf05I3r+B251/faSykv/ezbu2\nwkrn3NiyqI9UbpV5In4F3qHCk/CuwAiAc25WGWa2A14BWjvngjwPWTnKUU7pyhxf1FNAD+dczYMh\nQznKUc5+58wBznbedVY64u35us85918Luc2pcuKfU5HqopxA8j7HO3d7OgW33wM5tTQkZyowEe+8\n8K54V5yf45wL7EgPkUp7sTa8Q6auxru/YugVoAO532E+887VPgtvj15PvIuBPBRkhnKUo5xSi8fd\nDeJ1BwXlKEc5pRevO5Aop3xmKKf85+R7sAzKjORS4ArgT867mO8RwONxypZKojJPxC8EWjjn8sqi\ncPPOXbsc7wrj0/EucHWDc26bcpSjnPKRQ3zubhCvOygoRznKKb143YFEOeUzQznlPwe//EJjQVlw\n3t1nngr5ezk6R1wCVpkn4nPxbk2ytozKvw/4JzDYebeLKCvKUY5ySm8J/n2Jwznnuh5EGcpRjnL2\nzyagKbA4pPwsMzsL777LyjlwORWpLsrZT2aWxb497VXxzkvf5pyrE3BOJ7wLOh/v5yQC2c65ukHm\nSOVWmc8RnwK0BmZQhueYiEj5ZWZ34B323gR4B+8Ch3MOtgzlKEc5yqmoORWpLsoJnpldAHR0zt0X\ncLkz8er1b7wrtfcDWgadI5VbZZ6Id4v0eLwOeRGR8sPicHX2eGQoRznKKZOceN2BRDkHOEM55T+n\niOxpzrlOAZc50znXwczmOeda+49945zrHGSOVG6VdiIuIhKJxeEq8PHIUI5ylKOcippTkeqinJjL\nvijkzwS8vdXdnHOnBZzzBdALeBnIBFbj3ZqtTZA5UrklHOg3EG9m9pX//ywz2xryk2VmWw/0+xOR\n+DOzJDM718zeBD4CfgX6HmwZylGOcpRTUXMqUl2Us1/ODfk5E8gCzi+DnKvxzgu/FdgGHE7Z1Ecq\nsUq3R9zK4J6GInJwsshXZ3/fBXh19nhkKEc5ylFORc2pSHVRjoiEqowT8VnOufYH+n2IyIFnZp/j\nXZ393bK6Ons8MpSjHOUop6LmVKS6KCeQvMPwrmbeBe/q6V8Bdzjnfg+o/PlEuf95/vniIkGojBPx\n3wm5L2A451yRz4mIiIiIyIFhZpPxJv5v+A9dBVzpnOsdUPktgcbAirCnjgRWOecWBZEjApXwHHG8\n8z1qAbWL+BERERERkfKnoXPuVefcLv/nNaBhgOU/DWx1zv0W+gNs958TCUyVA/0GDoDVzrmHD/Sb\nEBERERGRmKw3s6vwbo8G3vnpGwIsv5lzbl74g865IMFz+QAAAkdJREFUmWbWLMAckUq5R9wO9BsQ\nEREREZGYXQdcwr5biv3Rfywo1aI8Vz3AHJFKeY54cjwuJiEiIiIiIgcPM3sL+Mw591LY438C+jjn\nLj0w70wqoko3ERcRERERkYOPmTUHbgOaEXKKrXPuvIDKbwy8B+QB3/sPdwCqAhc65zKDyBEBTcRF\nREREROQgYGZzgbHAfGBP/uPOuakB56QBrfw/f3DOfRZk+SKgibiIiIiIiBwEzOw759ypB/p9iARB\nE3ERERERESn3zOwKoCUwCcjNf9w5N+uAvSmRUqqMty8TEREREZGDz0nA1UAP9h2a7vy/RQ4q2iMu\nIiIiIiLlnpn9DLR2zuUd6Pcisr8q433ERURERETk4DMXqHeg34RIEHRouoiIiIiIHAwaAz+b2Qz2\nnSPunHPnH8D3JFIqOjRdRERERETKPTPrFvoncDpwuXPuxAP0lkRKTYemi4iIiIhIueffL3wLcA7w\nGtATeOFAvieR0tKh6SIiIiIiUm6Z2THAZcDlwAbgHbwje9MO6BsT2Q86NF1ERERERMotM9sDfAn8\nyTm3yH9siXOuxYF9ZyKlp0PTRURERESkPOsLZAKfm9lLZtYT7xxxkYOW9oiLiIiIiEi5Z2Y1gQvw\nDlHvAYwD3nPOTTqgb0ykFDQRFxERERGRg4qZJQMXA5c653oc6PcjEitNxEVERERERETiSOeIi4iI\niIiIiMSRJuIiIiIiIiIicaSJuIiIiIiIiEgcaSIuIiIiIiIiEkeaiIuIiIiIiIjE0f8H8rgWN/Cg\nYYYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8f517d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation heatmap\n",
    "corr = df.corr()\n",
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(corr, annot=True, fmt=\".1f\")\n",
    "plt.title(\"Correlation Heatmap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Transaction Amount in $')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cmGQRcDywbsAxSZIkzarW9JxV1YNJTgEuBhYAZ1XVxgGHJUmSNKtak5wBVNV6YP2g4xhj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\nktQiJmeSJEktYnImSZLUIv8/fyNFKsUgUOUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8875f85f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature - \"Amount\" - comparing transaction amount of fraud & non-fraud transactions\n",
    "fig, (ax1, ax2) =plt.subplots(nrows=2,ncols=1, sharex=True, figsize=(10,6))\n",
    "\n",
    "ax1.hist(df.Amount[df.Class==1], bins=25, color=\"Red\")\n",
    "ax1.set_title(\"fraud\")\n",
    "\n",
    "ax2.hist(df.Amount[df.Class==0], bins=25, color=\"Green\")\n",
    "ax2.set_title(\"non-fraud\")\n",
    "\n",
    "plt.yscale('log', nonposy='clip')\n",
    "plt.ylabel(\"Number of Transactions\")\n",
    "plt.xlabel(\"Transaction Amount in $\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f88802a208>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x1saxymM38XjOpUGLiCWZ2eSPZ33OlClJkmpr51MekjR6mo1GSGobRygkSVJt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\nJElSbQYUkiSpNgMKSZJUmwGFJEmq7f8DA/fI3DyEEFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f887630c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all transactions over time\n",
    "bins=80\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.hist(df.Time[df.Class==1],bins=bins, density=True, alpha=0.8,label='Fraud',color='Red')\n",
    "plt.hist(df.Time[df.Class==0],bins=bins, density=True, alpha=0.8,label='Not Fraud',color='Green')\n",
    "plt.title('Transactions over Time')\n",
    "plt.xlabel(\"% of Transactions\")\n",
    "plt.ylabel(\"time\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# compare features V1-V28 of fraud and non-fraud transactions\n",
    "x = df.drop([\"Class\", \"Time\", \"Amount\"], axis=1)\n",
    "y = df.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f88862a048>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kcJ9WBXTB5/UqIyPD7TLQTxyQ2aRlSz93u4ykQXjaPfSgAEDichKeaowxQyRZSTLGHCWJ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\nLcmNMRft7eHt8E4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8886507b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# have a look at the first ten features: V1-V10\n",
    "first10=pd.concat([y,x.iloc[:,0:10]],axis=1)\n",
    "first10=pd.melt(first10,id_vars=\"Class\",var_name=\"Feature\",value_name='Value')\n",
    "plt.figure(figsize=(14,6))\n",
    "sns.violinplot(x=\"Feature\",y=\"Value\",hue=\"Class\",data=first10, split=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V19</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.935192</td>\n",
       "      <td>0.766490</td>\n",
       "      <td>0.881365</td>\n",
       "      <td>0.313023</td>\n",
       "      <td>0.763439</td>\n",
       "      <td>0.267669</td>\n",
       "      <td>0.266815</td>\n",
       "      <td>0.786444</td>\n",
       "      <td>0.475312</td>\n",
       "      <td>0.510600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.594863</td>\n",
       "      <td>0.582942</td>\n",
       "      <td>0.561184</td>\n",
       "      <td>0.522992</td>\n",
       "      <td>0.663793</td>\n",
       "      <td>0.391253</td>\n",
       "      <td>0.585122</td>\n",
       "      <td>0.394557</td>\n",
       "      <td>0.418976</td>\n",
       "      <td>0.312697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.978542</td>\n",
       "      <td>0.770067</td>\n",
       "      <td>0.840298</td>\n",
       "      <td>0.271796</td>\n",
       "      <td>0.766120</td>\n",
       "      <td>0.262192</td>\n",
       "      <td>0.264875</td>\n",
       "      <td>0.786298</td>\n",
       "      <td>0.453981</td>\n",
       "      <td>0.505267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.551930</td>\n",
       "      <td>0.579530</td>\n",
       "      <td>0.557840</td>\n",
       "      <td>0.480237</td>\n",
       "      <td>0.666938</td>\n",
       "      <td>0.336440</td>\n",
       "      <td>0.587290</td>\n",
       "      <td>0.446013</td>\n",
       "      <td>0.416345</td>\n",
       "      <td>0.313423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.935217</td>\n",
       "      <td>0.753118</td>\n",
       "      <td>0.868141</td>\n",
       "      <td>0.268766</td>\n",
       "      <td>0.762329</td>\n",
       "      <td>0.281122</td>\n",
       "      <td>0.270177</td>\n",
       "      <td>0.788042</td>\n",
       "      <td>0.410603</td>\n",
       "      <td>0.513018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386683</td>\n",
       "      <td>0.585855</td>\n",
       "      <td>0.565477</td>\n",
       "      <td>0.546030</td>\n",
       "      <td>0.678939</td>\n",
       "      <td>0.289354</td>\n",
       "      <td>0.559515</td>\n",
       "      <td>0.402727</td>\n",
       "      <td>0.415489</td>\n",
       "      <td>0.311911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.941878</td>\n",
       "      <td>0.765304</td>\n",
       "      <td>0.868484</td>\n",
       "      <td>0.213661</td>\n",
       "      <td>0.765647</td>\n",
       "      <td>0.275559</td>\n",
       "      <td>0.266803</td>\n",
       "      <td>0.789434</td>\n",
       "      <td>0.414999</td>\n",
       "      <td>0.507585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.467058</td>\n",
       "      <td>0.578050</td>\n",
       "      <td>0.559734</td>\n",
       "      <td>0.510277</td>\n",
       "      <td>0.662607</td>\n",
       "      <td>0.223826</td>\n",
       "      <td>0.614245</td>\n",
       "      <td>0.389197</td>\n",
       "      <td>0.417669</td>\n",
       "      <td>0.314371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.938617</td>\n",
       "      <td>0.776520</td>\n",
       "      <td>0.864251</td>\n",
       "      <td>0.269796</td>\n",
       "      <td>0.762975</td>\n",
       "      <td>0.263984</td>\n",
       "      <td>0.268968</td>\n",
       "      <td>0.782484</td>\n",
       "      <td>0.490950</td>\n",
       "      <td>0.524303</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626060</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.561327</td>\n",
       "      <td>0.547271</td>\n",
       "      <td>0.663392</td>\n",
       "      <td>0.401270</td>\n",
       "      <td>0.566343</td>\n",
       "      <td>0.507497</td>\n",
       "      <td>0.420561</td>\n",
       "      <td>0.317490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0  0.935192  0.766490  0.881365  0.313023  0.763439  0.267669  0.266815   \n",
       "1  0.978542  0.770067  0.840298  0.271796  0.766120  0.262192  0.264875   \n",
       "2  0.935217  0.753118  0.868141  0.268766  0.762329  0.281122  0.270177   \n",
       "3  0.941878  0.765304  0.868484  0.213661  0.765647  0.275559  0.266803   \n",
       "4  0.938617  0.776520  0.864251  0.269796  0.762975  0.263984  0.268968   \n",
       "\n",
       "         V8        V9       V10    ...          V19       V20       V21  \\\n",
       "0  0.786444  0.475312  0.510600    ...     0.594863  0.582942  0.561184   \n",
       "1  0.786298  0.453981  0.505267    ...     0.551930  0.579530  0.557840   \n",
       "2  0.788042  0.410603  0.513018    ...     0.386683  0.585855  0.565477   \n",
       "3  0.789434  0.414999  0.507585    ...     0.467058  0.578050  0.559734   \n",
       "4  0.782484  0.490950  0.524303    ...     0.626060  0.584615  0.561327   \n",
       "\n",
       "        V22       V23       V24       V25       V26       V27       V28  \n",
       "0  0.522992  0.663793  0.391253  0.585122  0.394557  0.418976  0.312697  \n",
       "1  0.480237  0.666938  0.336440  0.587290  0.446013  0.416345  0.313423  \n",
       "2  0.546030  0.678939  0.289354  0.559515  0.402727  0.415489  0.311911  \n",
       "3  0.510277  0.662607  0.223826  0.614245  0.389197  0.417669  0.314371  \n",
       "4  0.547271  0.663392  0.401270  0.566343  0.507497  0.420561  0.317490  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to make the feature difference more visible between fraud & non-fraud transactions, let's scale feature values\n",
    "x_scaled=(x-x.min())/(x.max()-x.min())\n",
    "x_scaled.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature1_10=pd.concat([y,x_scaled.iloc[:,0:10]],axis=1)\n",
    "feature11_20=pd.concat([y,x_scaled.iloc[:,10:19]],axis=1)\n",
    "feature21_28=pd.concat([y,x_scaled.iloc[:,19:28]],axis=1)\n",
    "feature1_10=pd.melt(feature1_10,id_vars=\"Class\",var_name=\"Feature\",value_name='Value')\n",
    "feature11_20=pd.melt(feature11_20,id_vars=\"Class\",var_name=\"Feature\",value_name='Value')\n",
    "feature21_28=pd.melt(feature21_28,id_vars=\"Class\",var_name=\"Feature\",value_name='Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature V1-V10')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6TCGEEEKI9mRIiGgpr6zmaMYVkdth2cYeVwkGgzzwwAOsW7eOrKws5s6dy9KlS5k2bVpE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f888650c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "sns.violinplot(x=\"Feature\",y=\"Value\",hue=\"Class\",data=feature1_10, split=True)\n",
    "plt.title(\"Feature V1-V10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature V11-V2O')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J2Lx5c9QlQ9/4xjeYP//fqJpCDE8KoeTBGHGpvZoUHTm9YOn8t0mPM4fRRc+1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\n8f2xtCR8y5SUlHDbrbdC3V4yt3ze7N9bU+Hjrdjmwcccc4yFEbrf2j3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2hgTbVkTkHhFZ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\nDCmllFJKKaXSkiZDSimllFJKqbT0/wHmer+vEXtATQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f880c2ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "sns.violinplot(x=\"Feature\",y=\"Value\",hue=\"Class\",data=feature11_20, split=True)\n",
    "plt.title(\"Feature V11-V2O\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature V21-V28')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aoLUuO2wjrd8C3gI488wzj9xHSDkcDnbs2MGOHTvIyclh2/bt7Nm9+6iuMIOS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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f880c843c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "sns.violinplot(x=\"Feature\",y=\"Value\",hue=\"Class\",data=feature21_28, split=True)\n",
    "plt.title(\"Feature V21-V28\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build A Linear Classifier With Adagrad Optimizer\n",
    "- Adagrad Optimizer can automaticallty adjust learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop features that have very little difference between fraud and non-fraud transactions\n",
    "X = df.drop(['V28','V27','V26','V25','V24','V23','V22','V20','V15','V13','V8','Time','Class'],axis=1)\n",
    "# standardization of feauture \"Amount\"\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "X.Amount= StandardScaler().fit_transform(X['Amount'].values.reshape(-1, 1))\n",
    "y = df.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split traing and test datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test=train_test_split(X,y, test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define feature columns in tensorflow\n",
    "V01 = tf.feature_column.numeric_column('V1')\n",
    "V02 = tf.feature_column.numeric_column('V2')\n",
    "V03 = tf.feature_column.numeric_column('V3')\n",
    "V04 = tf.feature_column.numeric_column('V4')\n",
    "V05 = tf.feature_column.numeric_column('V5')\n",
    "V06 = tf.feature_column.numeric_column('V6')\n",
    "V07 = tf.feature_column.numeric_column('V7')\n",
    "V09 = tf.feature_column.numeric_column('V9') \n",
    "V10 = tf.feature_column.numeric_column('V10')\n",
    "V11 = tf.feature_column.numeric_column('V11')\n",
    "V12 = tf.feature_column.numeric_column('V12')\n",
    "V14 = tf.feature_column.numeric_column('V14')\n",
    "V16 = tf.feature_column.numeric_column('V16')\n",
    "V17 = tf.feature_column.numeric_column('V17')\n",
    "V18 = tf.feature_column.numeric_column('V18')\n",
    "V19 = tf.feature_column.numeric_column('V19')\n",
    "V21 = tf.feature_column.numeric_column('V21')\n",
    "V22 = tf.feature_column.numeric_column('V22')\n",
    "Vamt = tf.feature_column.numeric_column('Amount')\n",
    "\n",
    "features=[V01,V02,V03,V04,V05,V06,V07,V09,V10,V11,V12,V14,V16,V17,V18,V19,V21,Vamt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define input function would feed Pandas DataFrame into the model\n",
    "input=tf.estimator.inputs.pandas_input_fn(x=x_train,y=y_train,batch_size=100,num_epochs=1000,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\mibiy\\AppData\\Local\\Temp\\tmp_u3gqzwi\n",
      "INFO:tensorflow:Using config: {'_model_dir': 'C:\\\\Users\\\\mibiy\\\\AppData\\\\Local\\\\Temp\\\\tmp_u3gqzwi', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001F8884DD630>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "# define classifer - linear classifier\n",
    "model=tf.estimator.LinearClassifier(feature_columns=features,n_classes=2, optimizer=\"Adagrad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into C:\\Users\\mibiy\\AppData\\Local\\Temp\\tmp_u3gqzwi\\model.ckpt.\n",
      "INFO:tensorflow:loss = 69.31474, step = 1\n",
      "INFO:tensorflow:global_step/sec: 128.732\n",
      "INFO:tensorflow:loss = 8.118046, step = 101 (0.790 sec)\n",
      "INFO:tensorflow:global_step/sec: 147.413\n",
      "INFO:tensorflow:loss = 4.4544487, step = 201 (0.674 sec)\n",
      "INFO:tensorflow:global_step/sec: 152.007\n",
      "INFO:tensorflow:loss = 3.1202888, step = 301 (0.649 sec)\n",
      "INFO:tensorflow:global_step/sec: 139.07\n",
      "INFO:tensorflow:loss = 2.4283116, step = 401 (0.726 sec)\n",
      "INFO:tensorflow:global_step/sec: 132.229\n",
      "INFO:tensorflow:loss = 2.0206318, step = 501 (0.758 sec)\n",
      "INFO:tensorflow:global_step/sec: 144.782\n",
      "INFO:tensorflow:loss = 1.6640524, step = 601 (0.698 sec)\n",
      "INFO:tensorflow:global_step/sec: 139.255\n",
      "INFO:tensorflow:loss = 1.4292719, step = 701 (0.710 sec)\n",
      "INFO:tensorflow:global_step/sec: 150.784\n",
      "INFO:tensorflow:loss = 1.3029375, step = 801 (0.663 sec)\n",
      "INFO:tensorflow:global_step/sec: 138.764\n",
      "INFO:tensorflow:loss = 1.2424921, step = 901 (0.722 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 1000 into C:\\Users\\mibiy\\AppData\\Local\\Temp\\tmp_u3gqzwi\\model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 1.885289.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.linear.LinearClassifier at 0x1f8884dd2b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model\n",
    "model.train(input_fn=input,steps=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2018-07-22-02:31:25\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\mibiy\\AppData\\Local\\Temp\\tmp_u3gqzwi\\model.ckpt-1000\n",
      "INFO:tensorflow:Finished evaluation at 2018-07-22-02:32:59\n",
      "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.9992025, accuracy_baseline = 0.9982745, auc = 0.9763705, auc_precision_recall = 0.73565185, average_loss = 0.013306155, global_step = 1000, label/mean = 0.0017254871, loss = 0.13305755, prediction/mean = 0.011701868\n",
      "{'accuracy': 0.9992025, 'accuracy_baseline': 0.9982745, 'auc': 0.9763705, 'auc_precision_recall': 0.73565185, 'average_loss': 0.013306155, 'label/mean': 0.0017254871, 'loss': 0.13305755, 'prediction/mean': 0.011701868, 'global_step': 1000}\n"
     ]
    }
   ],
   "source": [
    "# evaluate model performance with training data\n",
    "result_train=model.evaluate(tf.estimator.inputs.pandas_input_fn(x=x_train,y=y_train,batch_size=10, num_epochs=1, shuffle=False))\n",
    "print(result_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2018-07-22-02:33:06\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\mibiy\\AppData\\Local\\Temp\\tmp_u3gqzwi\\model.ckpt-1000\n",
      "INFO:tensorflow:Finished evaluation at 2018-07-22-02:33:43\n",
      "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.99921584, accuracy_baseline = 0.9982678, auc = 0.9675595, auc_precision_recall = 0.7791059, average_loss = 0.0128318155, global_step = 1000, label/mean = 0.0017321489, loss = 0.12830764, prediction/mean = 0.011621637\n",
      "{'accuracy': 0.99921584, 'accuracy_baseline': 0.9982678, 'auc': 0.9675595, 'auc_precision_recall': 0.7791059, 'average_loss': 0.0128318155, 'label/mean': 0.0017321489, 'loss': 0.12830764, 'prediction/mean': 0.011621637, 'global_step': 1000}\n"
     ]
    }
   ],
   "source": [
    "# evaluate model performance with testing data\n",
    "input_test = tf.estimator.inputs.pandas_input_fn(x=x_test,y=y_test,batch_size=10, num_epochs=1, shuffle=False)\n",
    "result_test=model.evaluate(input_test)\n",
    "print(result_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from C:\\Users\\mibiy\\AppData\\Local\\Temp\\tmp_u3gqzwi\\model.ckpt-1000\n"
     ]
    },
    {
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       " array([-4.4576564], dtype=float32),\n",
       " array([-4.68662], dtype=float32),\n",
       " array([-4.5428514], dtype=float32),\n",
       " array([-4.8321266], dtype=float32),\n",
       " array([-4.5669336], dtype=float32),\n",
       " array([-4.455327], dtype=float32),\n",
       " array([-4.4441557], dtype=float32),\n",
       " array([-4.470799], dtype=float32),\n",
       " array([-4.5267076], dtype=float32),\n",
       " array([-4.801566], dtype=float32),\n",
       " array([-4.407678], dtype=float32),\n",
       " array([-4.684433], dtype=float32),\n",
       " array([-4.4561486], dtype=float32),\n",
       " array([-4.5956], dtype=float32),\n",
       " array([-4.656357], dtype=float32),\n",
       " array([-4.6973357], dtype=float32),\n",
       " array([-4.6317987], dtype=float32),\n",
       " array([-4.678403], dtype=float32),\n",
       " array([-4.5575294], dtype=float32),\n",
       " array([-4.5340295], dtype=float32),\n",
       " array([-4.6330976], dtype=float32),\n",
       " array([-4.6064725], dtype=float32),\n",
       " array([-3.5565813], dtype=float32),\n",
       " array([-4.773935], dtype=float32),\n",
       " array([-4.6509376], dtype=float32),\n",
       " array([-4.6729765], dtype=float32),\n",
       " array([-4.6089034], dtype=float32),\n",
       " array([-4.342841], dtype=float32),\n",
       " array([-4.7260118], dtype=float32),\n",
       " array([-4.7096763], dtype=float32),\n",
       " array([-5.008731], dtype=float32),\n",
       " array([-4.3157873], dtype=float32),\n",
       " array([-4.3721046], dtype=float32),\n",
       " array([-4.7466764], dtype=float32),\n",
       " ...]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction\n",
    "input_pred= tf.estimator.inputs.pandas_input_fn(x=x_test,batch_size=10,num_epochs=1,shuffle=False)\n",
    "prediction = model.predict(input_pred)\n",
    "y_pred = [d['logits'] for d in prediction]\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S1K8YyHrKppvCl75UugpJktQPGchWxssv13rDnn++tnz//bDttmVrkiRJ/ZaBbGXMmgXn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\nIDgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8a26e63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Curve\n",
    "from sklearn import metrics\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test, y_pred)\n",
    "roc_auc=metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.title('ROC Curve - Linear Classifier')\n",
    "plt.plot(fpr, tpr, 'r', label='Area Under Curve = %0.4f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Build A Neutral Network To Improve Prediction Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use all the attributes\n",
    "x_neural=df.drop(['Class'],axis=1)\n",
    "x_scaled_neural=(x_neural-x_neural.min())/(x_neural.max()-x_neural.min())\n",
    "y_neural=df.Class\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train_neural,x_test_neural,y_train_neural,y_test_neural=train_test_split(x_scaled_neural,y_neural,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# feed data\n",
    "def to_one_hot(c, depth):\n",
    "    i=np.identity(depth)\n",
    "    return i[c,:]\n",
    "\n",
    "def train_batch(batch_size):\n",
    "    for j in range(int(len(x_train_neural)/batch_size)):\n",
    "        start=batch_size*j\n",
    "        end=start+batch_size\n",
    "        \n",
    "        train_x_batch=x_train_neural[start:end]\n",
    "        train_y_batch=y_train_neural[start:end]\n",
    "        \n",
    "        train_y_batch=np.apply_along_axis(lambda x:to_one_hot(x,depth=2),0,train_y_batch)\n",
    "        \n",
    "        yield train_x_batch, train_y_batch\n",
    "        \n",
    "def get_test_data():\n",
    "    return x_test_neural, np.apply_along_axis(lambda x: to_one_hot(x, depth=2), 0, y_test_neural)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1 loss: 169.0390569618992\n",
      "Epoch 2 loss: 8.083136622837173\n",
      "Epoch 3 loss: 7.704049995430523\n"
     ]
    },
    {
     "data": {
      "image/png": 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EISIiSaWUKMxsjpntMLOdZvZAO/vPN7PVZrbFzCJmNjZu36NmtjV83BpXfrWZ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\n+0hfZrbR3dPquxqKOT0o5vT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8ba8ac2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Precision: 0.9998728982126247\n"
     ]
    }
   ],
   "source": [
    "# define model\n",
    "X= tf.placeholder(tf.float32, [None, 30])\n",
    "Y= tf.placeholder(tf.float32, [None, 2]) \n",
    "\n",
    "def forward_propogation(X): \n",
    "    num_neural= 10\n",
    "    weights={\"lvl_1\": tf.Variable(tf.random_normal([30,num_neural])), \n",
    "               \"output\": tf.Variable(tf.random_normal([num_neural,2]))}\n",
    "    \n",
    "    biases={\"lvl_1\": tf.Variable(tf.random_normal([num_neural])), \n",
    "            \"output\": tf.Variable(tf.random_normal([2]))}\n",
    "    \n",
    "    h1=tf.add(tf.matmul(X, weights[\"lvl_1\"]), biases[\"lvl_1\"])\n",
    "    h1=tf.nn.relu(h1)\n",
    "    \n",
    "    h2=tf.add(tf.matmul(h1, weights[\"output\"]), biases[\"output\"])\n",
    "    output=tf.nn.relu(h2)\n",
    "    \n",
    "    return output\n",
    "\n",
    "# Minimize loss\n",
    "logits=forward_propogation(X)\n",
    "loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=logits))\n",
    "optimizer=tf.train.AdamOptimizer(0.01).minimize(loss) \n",
    "\n",
    "# Confusion matrix accuracy\n",
    "correct = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1)) \n",
    "accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) \n",
    "\n",
    "# Precision-recall curve\n",
    "from sklearn.metrics import average_precision_score, precision_recall_curve\n",
    "\n",
    "decision_variable = tf.nn.softmax(logits) \n",
    "\n",
    "with tf.Session() as sess: \n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    # train phase\n",
    "    batch_size = 128\n",
    "    n_epochs = 3\n",
    "    for epoch in range(n_epochs):\n",
    "        epoch_loss = 0\n",
    "        batch_generator = train_batch(batch_size)\n",
    "        for batch in batch_generator:\n",
    "            batch_x, batch_t = batch \n",
    "            _, curr_loss = sess.run([optimizer, loss], feed_dict={Y: batch_t, X: batch_x})\n",
    "            epoch_loss += curr_loss\n",
    "            \n",
    "        print(\"Epoch \" + str(epoch+1) + \" loss: \" + str(epoch_loss))\n",
    "        \n",
    "    # test phase\n",
    "    test_x, test_t = get_test_data()\n",
    "    test_y = sess.run(decision_variable, feed_dict={X: test_x})\n",
    "    \n",
    "    avg_score = average_precision_score(test_t[:,0], test_y[:,0])\n",
    "    precision, recall, _ = precision_recall_curve(test_t[:,0], test_y[:,0])\n",
    "\n",
    "    # plot pr curve\n",
    "    plt.plot(recall, precision)\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    \n",
    "    # print average precision\n",
    "    print(\"Average Precision:\", avg_score)"
   ]
  }
 ],
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